{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 实战练习之一 AI股票拟合算法"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 输入必要的库"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import pandas            as pd\n",
    "import tensorflow        as tf  \n",
    "import numpy             as np\n",
    "import matplotlib.pyplot as plt\n",
    "# 支持中文\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei']  # 用来正常显示中文标签\n",
    "plt.rcParams['axes.unicode_minus'] = False  # 用来正常显示负号\n",
    "\n",
    "from numpy                 import array\n",
    "from sklearn               import metrics\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from tensorflow.keras.models          import Sequential\n",
    "from tensorflow.keras.layers          import Dense,LSTM,Bidirectional\n",
    "\n",
    "from numpy.random   import seed\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['ALLOW_THREADS',\n",
       " 'BUFSIZE',\n",
       " 'CLIP',\n",
       " 'DataSource',\n",
       " 'ERR_CALL',\n",
       " 'ERR_DEFAULT',\n",
       " 'ERR_IGNORE',\n",
       " 'ERR_LOG',\n",
       " 'ERR_PRINT',\n",
       " 'ERR_RAISE',\n",
       " 'ERR_WARN',\n",
       " 'FLOATING_POINT_SUPPORT',\n",
       " 'FPE_DIVIDEBYZERO',\n",
       " 'FPE_INVALID',\n",
       " 'FPE_OVERFLOW',\n",
       " 'FPE_UNDERFLOW',\n",
       " 'False_',\n",
       " 'Inf',\n",
       " 'Infinity',\n",
       " 'MAXDIMS',\n",
       " 'MAY_SHARE_BOUNDS',\n",
       " 'MAY_SHARE_EXACT',\n",
       " 'NAN',\n",
       " 'NINF',\n",
       " 'NZERO',\n",
       " 'NaN',\n",
       " 'PINF',\n",
       " 'PZERO',\n",
       " 'RAISE',\n",
       " 'RankWarning',\n",
       " 'SHIFT_DIVIDEBYZERO',\n",
       " 'SHIFT_INVALID',\n",
       " 'SHIFT_OVERFLOW',\n",
       " 'SHIFT_UNDERFLOW',\n",
       " 'ScalarType',\n",
       " 'True_',\n",
       " 'UFUNC_BUFSIZE_DEFAULT',\n",
       " 'UFUNC_PYVALS_NAME',\n",
       " 'WRAP',\n",
       " '_CopyMode',\n",
       " '_NoValue',\n",
       " '_UFUNC_API',\n",
       " '__NUMPY_SETUP__',\n",
       " '__all__',\n",
       " '__builtins__',\n",
       " '__cached__',\n",
       " '__config__',\n",
       " '__deprecated_attrs__',\n",
       " '__dir__',\n",
       " '__doc__',\n",
       " '__expired_functions__',\n",
       " '__file__',\n",
       " '__former_attrs__',\n",
       " '__future_scalars__',\n",
       " '__getattr__',\n",
       " '__loader__',\n",
       " '__name__',\n",
       " '__package__',\n",
       " '__path__',\n",
       " '__spec__',\n",
       " '__version__',\n",
       " '_add_newdoc_ufunc',\n",
       " '_builtins',\n",
       " '_core',\n",
       " '_distributor_init',\n",
       " '_financial_names',\n",
       " '_get_promotion_state',\n",
       " '_globals',\n",
       " '_int_extended_msg',\n",
       " '_mat',\n",
       " '_no_nep50_warning',\n",
       " '_pyinstaller_hooks_dir',\n",
       " '_pytesttester',\n",
       " '_set_promotion_state',\n",
       " '_specific_msg',\n",
       " '_typing',\n",
       " '_using_numpy2_behavior',\n",
       " '_utils',\n",
       " 'abs',\n",
       " 'absolute',\n",
       " 'add',\n",
       " 'add_docstring',\n",
       " 'add_newdoc',\n",
       " 'add_newdoc_ufunc',\n",
       " 'all',\n",
       " 'allclose',\n",
       " 'alltrue',\n",
       " 'amax',\n",
       " 'amin',\n",
       " 'angle',\n",
       " 'any',\n",
       " 'append',\n",
       " 'apply_along_axis',\n",
       " 'apply_over_axes',\n",
       " 'arange',\n",
       " 'arccos',\n",
       " 'arccosh',\n",
       " 'arcsin',\n",
       " 'arcsinh',\n",
       " 'arctan',\n",
       " 'arctan2',\n",
       " 'arctanh',\n",
       " 'argmax',\n",
       " 'argmin',\n",
       " 'argpartition',\n",
       " 'argsort',\n",
       " 'argwhere',\n",
       " 'around',\n",
       " 'array',\n",
       " 'array2string',\n",
       " 'array_equal',\n",
       " 'array_equiv',\n",
       " 'array_repr',\n",
       " 'array_split',\n",
       " 'array_str',\n",
       " 'asanyarray',\n",
       " 'asarray',\n",
       " 'asarray_chkfinite',\n",
       " 'ascontiguousarray',\n",
       " 'asfarray',\n",
       " 'asfortranarray',\n",
       " 'asmatrix',\n",
       " 'atleast_1d',\n",
       " 'atleast_2d',\n",
       " 'atleast_3d',\n",
       " 'average',\n",
       " 'bartlett',\n",
       " 'base_repr',\n",
       " 'binary_repr',\n",
       " 'bincount',\n",
       " 'bitwise_and',\n",
       " 'bitwise_not',\n",
       " 'bitwise_or',\n",
       " 'bitwise_xor',\n",
       " 'blackman',\n",
       " 'block',\n",
       " 'bmat',\n",
       " 'bool_',\n",
       " 'broadcast',\n",
       " 'broadcast_arrays',\n",
       " 'broadcast_shapes',\n",
       " 'broadcast_to',\n",
       " 'busday_count',\n",
       " 'busday_offset',\n",
       " 'busdaycalendar',\n",
       " 'byte',\n",
       " 'byte_bounds',\n",
       " 'bytes_',\n",
       " 'c_',\n",
       " 'can_cast',\n",
       " 'cast',\n",
       " 'cbrt',\n",
       " 'cdouble',\n",
       " 'ceil',\n",
       " 'cfloat',\n",
       " 'char',\n",
       " 'character',\n",
       " 'chararray',\n",
       " 'choose',\n",
       " 'clip',\n",
       " 'clongdouble',\n",
       " 'clongfloat',\n",
       " 'column_stack',\n",
       " 'common_type',\n",
       " 'compare_chararrays',\n",
       " 'compat',\n",
       " 'complex128',\n",
       " 'complex64',\n",
       " 'complex_',\n",
       " 'complexfloating',\n",
       " 'compress',\n",
       " 'concatenate',\n",
       " 'conj',\n",
       " 'conjugate',\n",
       " 'convolve',\n",
       " 'copy',\n",
       " 'copysign',\n",
       " 'copyto',\n",
       " 'corrcoef',\n",
       " 'correlate',\n",
       " 'cos',\n",
       " 'cosh',\n",
       " 'count_nonzero',\n",
       " 'cov',\n",
       " 'cross',\n",
       " 'csingle',\n",
       " 'ctypeslib',\n",
       " 'cumprod',\n",
       " 'cumproduct',\n",
       " 'cumsum',\n",
       " 'datetime64',\n",
       " 'datetime_as_string',\n",
       " 'datetime_data',\n",
       " 'deg2rad',\n",
       " 'degrees',\n",
       " 'delete',\n",
       " 'deprecate',\n",
       " 'deprecate_with_doc',\n",
       " 'diag',\n",
       " 'diag_indices',\n",
       " 'diag_indices_from',\n",
       " 'diagflat',\n",
       " 'diagonal',\n",
       " 'diff',\n",
       " 'digitize',\n",
       " 'disp',\n",
       " 'divide',\n",
       " 'divmod',\n",
       " 'dot',\n",
       " 'double',\n",
       " 'dsplit',\n",
       " 'dstack',\n",
       " 'dtype',\n",
       " 'dtypes',\n",
       " 'e',\n",
       " 'ediff1d',\n",
       " 'einsum',\n",
       " 'einsum_path',\n",
       " 'emath',\n",
       " 'empty',\n",
       " 'empty_like',\n",
       " 'equal',\n",
       " 'errstate',\n",
       " 'euler_gamma',\n",
       " 'exceptions',\n",
       " 'exp',\n",
       " 'exp2',\n",
       " 'expand_dims',\n",
       " 'expm1',\n",
       " 'expm1x',\n",
       " 'extract',\n",
       " 'eye',\n",
       " 'fabs',\n",
       " 'fastCopyAndTranspose',\n",
       " 'fft',\n",
       " 'fill_diagonal',\n",
       " 'find_common_type',\n",
       " 'finfo',\n",
       " 'fix',\n",
       " 'flatiter',\n",
       " 'flatnonzero',\n",
       " 'flexible',\n",
       " 'flip',\n",
       " 'fliplr',\n",
       " 'flipud',\n",
       " 'float16',\n",
       " 'float32',\n",
       " 'float64',\n",
       " 'float_',\n",
       " 'float_power',\n",
       " 'floating',\n",
       " 'floor',\n",
       " 'floor_divide',\n",
       " 'fmax',\n",
       " 'fmin',\n",
       " 'fmod',\n",
       " 'format_float_positional',\n",
       " 'format_float_scientific',\n",
       " 'format_parser',\n",
       " 'frexp',\n",
       " 'from_dlpack',\n",
       " 'frombuffer',\n",
       " 'fromfile',\n",
       " 'fromfunction',\n",
       " 'fromiter',\n",
       " 'frompyfunc',\n",
       " 'fromregex',\n",
       " 'fromstring',\n",
       " 'full',\n",
       " 'full_like',\n",
       " 'gcd',\n",
       " 'generic',\n",
       " 'genfromtxt',\n",
       " 'geomspace',\n",
       " 'get_array_wrap',\n",
       " 'get_include',\n",
       " 'get_printoptions',\n",
       " 'getbufsize',\n",
       " 'geterr',\n",
       " 'geterrcall',\n",
       " 'geterrobj',\n",
       " 'gradient',\n",
       " 'greater',\n",
       " 'greater_equal',\n",
       " 'half',\n",
       " 'hamming',\n",
       " 'hanning',\n",
       " 'heaviside',\n",
       " 'histogram',\n",
       " 'histogram2d',\n",
       " 'histogram_bin_edges',\n",
       " 'histogramdd',\n",
       " 'hsplit',\n",
       " 'hstack',\n",
       " 'hypot',\n",
       " 'i0',\n",
       " 'identity',\n",
       " 'iinfo',\n",
       " 'imag',\n",
       " 'in1d',\n",
       " 'index_exp',\n",
       " 'indices',\n",
       " 'inexact',\n",
       " 'inf',\n",
       " 'info',\n",
       " 'infty',\n",
       " 'inner',\n",
       " 'insert',\n",
       " 'int16',\n",
       " 'int32',\n",
       " 'int64',\n",
       " 'int8',\n",
       " 'int_',\n",
       " 'intc',\n",
       " 'integer',\n",
       " 'interp',\n",
       " 'intersect1d',\n",
       " 'intp',\n",
       " 'invert',\n",
       " 'is_busday',\n",
       " 'isclose',\n",
       " 'iscomplex',\n",
       " 'iscomplexobj',\n",
       " 'isfinite',\n",
       " 'isfortran',\n",
       " 'isin',\n",
       " 'isinf',\n",
       " 'isnan',\n",
       " 'isnat',\n",
       " 'isneginf',\n",
       " 'isposinf',\n",
       " 'isreal',\n",
       " 'isrealobj',\n",
       " 'isscalar',\n",
       " 'issctype',\n",
       " 'issubclass_',\n",
       " 'issubdtype',\n",
       " 'issubsctype',\n",
       " 'iterable',\n",
       " 'ix_',\n",
       " 'kaiser',\n",
       " 'kron',\n",
       " 'lcm',\n",
       " 'ldexp',\n",
       " 'left_shift',\n",
       " 'less',\n",
       " 'less_equal',\n",
       " 'lexsort',\n",
       " 'lib',\n",
       " 'linalg',\n",
       " 'linspace',\n",
       " 'little_endian',\n",
       " 'load',\n",
       " 'loadtxt',\n",
       " 'log',\n",
       " 'log10',\n",
       " 'log1p',\n",
       " 'log2',\n",
       " 'logaddexp',\n",
       " 'logaddexp2',\n",
       " 'logical_and',\n",
       " 'logical_not',\n",
       " 'logical_or',\n",
       " 'logical_xor',\n",
       " 'logspace',\n",
       " 'longcomplex',\n",
       " 'longdouble',\n",
       " 'longfloat',\n",
       " 'longlong',\n",
       " 'lookfor',\n",
       " 'ma',\n",
       " 'mask_indices',\n",
       " 'mat',\n",
       " 'matmul',\n",
       " 'matrix',\n",
       " 'max',\n",
       " 'maximum',\n",
       " 'maximum_sctype',\n",
       " 'may_share_memory',\n",
       " 'mean',\n",
       " 'median',\n",
       " 'memmap',\n",
       " 'meshgrid',\n",
       " 'mgrid',\n",
       " 'min',\n",
       " 'min_scalar_type',\n",
       " 'minimum',\n",
       " 'mintypecode',\n",
       " 'mod',\n",
       " 'modf',\n",
       " 'moveaxis',\n",
       " 'msort',\n",
       " 'multiply',\n",
       " 'nan',\n",
       " 'nan_to_num',\n",
       " 'nanargmax',\n",
       " 'nanargmin',\n",
       " 'nancumprod',\n",
       " 'nancumsum',\n",
       " 'nanmax',\n",
       " 'nanmean',\n",
       " 'nanmedian',\n",
       " 'nanmin',\n",
       " 'nanpercentile',\n",
       " 'nanprod',\n",
       " 'nanquantile',\n",
       " 'nanstd',\n",
       " 'nansum',\n",
       " 'nanvar',\n",
       " 'nbytes',\n",
       " 'ndarray',\n",
       " 'ndenumerate',\n",
       " 'ndim',\n",
       " 'ndindex',\n",
       " 'nditer',\n",
       " 'negative',\n",
       " 'nested_iters',\n",
       " 'newaxis',\n",
       " 'nextafter',\n",
       " 'nonzero',\n",
       " 'not_equal',\n",
       " 'numarray',\n",
       " 'number',\n",
       " 'obj2sctype',\n",
       " 'object_',\n",
       " 'ogrid',\n",
       " 'oldnumeric',\n",
       " 'ones',\n",
       " 'ones_like',\n",
       " 'outer',\n",
       " 'packbits',\n",
       " 'pad',\n",
       " 'partition',\n",
       " 'percentile',\n",
       " 'pi',\n",
       " 'piecewise',\n",
       " 'place',\n",
       " 'poly',\n",
       " 'poly1d',\n",
       " 'polyadd',\n",
       " 'polyder',\n",
       " 'polydiv',\n",
       " 'polyfit',\n",
       " 'polyint',\n",
       " 'polymul',\n",
       " 'polynomial',\n",
       " 'polysub',\n",
       " 'polyval',\n",
       " 'positive',\n",
       " 'power',\n",
       " 'printoptions',\n",
       " 'prod',\n",
       " 'product',\n",
       " 'promote_types',\n",
       " 'ptp',\n",
       " 'put',\n",
       " 'put_along_axis',\n",
       " 'putmask',\n",
       " 'quantile',\n",
       " 'r_',\n",
       " 'rad2deg',\n",
       " 'radians',\n",
       " 'random',\n",
       " 'ravel',\n",
       " 'ravel_multi_index',\n",
       " 'real',\n",
       " 'real_if_close',\n",
       " 'rec',\n",
       " 'recarray',\n",
       " 'recfromcsv',\n",
       " 'recfromtxt',\n",
       " 'reciprocal',\n",
       " 'record',\n",
       " 'remainder',\n",
       " 'repeat',\n",
       " 'require',\n",
       " 'reshape',\n",
       " 'resize',\n",
       " 'result_type',\n",
       " 'right_shift',\n",
       " 'rint',\n",
       " 'roll',\n",
       " 'rollaxis',\n",
       " 'roots',\n",
       " 'rot90',\n",
       " 'round',\n",
       " 'round_',\n",
       " 'row_stack',\n",
       " 's_',\n",
       " 'safe_eval',\n",
       " 'save',\n",
       " 'savetxt',\n",
       " 'savez',\n",
       " 'savez_compressed',\n",
       " 'sctype2char',\n",
       " 'sctypeDict',\n",
       " 'sctypes',\n",
       " 'searchsorted',\n",
       " 'select',\n",
       " 'set_numeric_ops',\n",
       " 'set_printoptions',\n",
       " 'set_string_function',\n",
       " 'setbufsize',\n",
       " 'setdiff1d',\n",
       " 'seterr',\n",
       " 'seterrcall',\n",
       " 'seterrobj',\n",
       " 'setxor1d',\n",
       " 'shape',\n",
       " 'shares_memory',\n",
       " 'short',\n",
       " 'show_config',\n",
       " 'show_runtime',\n",
       " 'sign',\n",
       " 'signbit',\n",
       " 'signedinteger',\n",
       " 'sin',\n",
       " 'sinc',\n",
       " 'single',\n",
       " 'singlecomplex',\n",
       " 'sinh',\n",
       " 'size',\n",
       " 'sometrue',\n",
       " 'sort',\n",
       " 'sort_complex',\n",
       " 'source',\n",
       " 'spacing',\n",
       " 'split',\n",
       " 'sqrt',\n",
       " 'square',\n",
       " 'squeeze',\n",
       " 'stack',\n",
       " 'std',\n",
       " 'str_',\n",
       " 'string_',\n",
       " 'subtract',\n",
       " 'sum',\n",
       " 'swapaxes',\n",
       " 'take',\n",
       " 'take_along_axis',\n",
       " 'tan',\n",
       " 'tanh',\n",
       " 'tensordot',\n",
       " 'test',\n",
       " 'testing',\n",
       " 'tile',\n",
       " 'timedelta64',\n",
       " 'trace',\n",
       " 'tracemalloc_domain',\n",
       " 'transpose',\n",
       " 'trapz',\n",
       " 'tri',\n",
       " 'tril',\n",
       " 'tril_indices',\n",
       " 'tril_indices_from',\n",
       " 'trim_zeros',\n",
       " 'triu',\n",
       " 'triu_indices',\n",
       " 'triu_indices_from',\n",
       " 'true_divide',\n",
       " 'trunc',\n",
       " 'typecodes',\n",
       " 'typename',\n",
       " 'typing',\n",
       " 'ubyte',\n",
       " 'ufunc',\n",
       " 'uint',\n",
       " 'uint16',\n",
       " 'uint32',\n",
       " 'uint64',\n",
       " 'uint8',\n",
       " 'uintc',\n",
       " 'uintp',\n",
       " 'ulonglong',\n",
       " 'unicode_',\n",
       " 'union1d',\n",
       " 'unique',\n",
       " 'unpackbits',\n",
       " 'unravel_index',\n",
       " 'unsignedinteger',\n",
       " 'unwrap',\n",
       " 'ushort',\n",
       " 'vander',\n",
       " 'var',\n",
       " 'vdot',\n",
       " 'vectorize',\n",
       " 'version',\n",
       " 'void',\n",
       " 'vsplit',\n",
       " 'vstack',\n",
       " 'where',\n",
       " 'who',\n",
       " 'zeros',\n",
       " 'zeros_like']"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.__all__\n",
    "np.__dict__\n",
    "np.__doc__\n",
    "np.__file__\n",
    "#np.__package__\n",
    "dir(np)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 导入股票模块"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 导入tushare\n",
    "import tushare as ts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['MailMerge',\n",
       " 'TraderAPI',\n",
       " '__author__',\n",
       " '__builtins__',\n",
       " '__cached__',\n",
       " '__doc__',\n",
       " '__file__',\n",
       " '__loader__',\n",
       " '__name__',\n",
       " '__package__',\n",
       " '__path__',\n",
       " '__spec__',\n",
       " '__version__',\n",
       " 'bar',\n",
       " 'bdi',\n",
       " 'broker_tops',\n",
       " 'cap_tops',\n",
       " 'close_apis',\n",
       " 'codecs',\n",
       " 'coins',\n",
       " 'coins_bar',\n",
       " 'coins_snapshot',\n",
       " 'coins_tick',\n",
       " 'coins_trade',\n",
       " 'day_boxoffice',\n",
       " 'day_cinema',\n",
       " 'forecast_data',\n",
       " 'fund',\n",
       " 'fund_holdings',\n",
       " 'futures',\n",
       " 'get_apis',\n",
       " 'get_area_classified',\n",
       " 'get_balance_sheet',\n",
       " 'get_cash_flow',\n",
       " 'get_cashflow_data',\n",
       " 'get_cffex_daily',\n",
       " 'get_concept_classified',\n",
       " 'get_cpi',\n",
       " 'get_czce_daily',\n",
       " 'get_day_all',\n",
       " 'get_dce_daily',\n",
       " 'get_debtpaying_data',\n",
       " 'get_deposit_rate',\n",
       " 'get_fund_info',\n",
       " 'get_future_daily',\n",
       " 'get_gdp_contrib',\n",
       " 'get_gdp_for',\n",
       " 'get_gdp_pull',\n",
       " 'get_gdp_quarter',\n",
       " 'get_gdp_year',\n",
       " 'get_gem_classified',\n",
       " 'get_gold_and_foreign_reserves',\n",
       " 'get_growth_data',\n",
       " 'get_h_data',\n",
       " 'get_hist_data',\n",
       " 'get_hists',\n",
       " 'get_hs300s',\n",
       " 'get_index',\n",
       " 'get_industry_classified',\n",
       " 'get_instrument',\n",
       " 'get_intlfuture',\n",
       " 'get_k_data',\n",
       " 'get_latest_news',\n",
       " 'get_loan_rate',\n",
       " 'get_markets',\n",
       " 'get_money_supply',\n",
       " 'get_money_supply_bal',\n",
       " 'get_nav_close',\n",
       " 'get_nav_grading',\n",
       " 'get_nav_history',\n",
       " 'get_nav_open',\n",
       " 'get_notices',\n",
       " 'get_operation_data',\n",
       " 'get_ppi',\n",
       " 'get_profit_data',\n",
       " 'get_profit_statement',\n",
       " 'get_realtime_quotes',\n",
       " 'get_report_data',\n",
       " 'get_rrr',\n",
       " 'get_shfe_daily',\n",
       " 'get_shfe_vwap',\n",
       " 'get_sina_dd',\n",
       " 'get_sme_classified',\n",
       " 'get_st_classified',\n",
       " 'get_stock_basics',\n",
       " 'get_suspended',\n",
       " 'get_sz50s',\n",
       " 'get_terminated',\n",
       " 'get_tick_data',\n",
       " 'get_today_all',\n",
       " 'get_today_ticks',\n",
       " 'get_token',\n",
       " 'get_zz500s',\n",
       " 'global_realtime',\n",
       " 'guba_sina',\n",
       " 'ht_subs',\n",
       " 'inst_detail',\n",
       " 'inst_tops',\n",
       " 'internet',\n",
       " 'is_holiday',\n",
       " 'latest_content',\n",
       " 'lpr_data',\n",
       " 'lpr_ma_data',\n",
       " 'margin_detail',\n",
       " 'margin_offset',\n",
       " 'margin_target',\n",
       " 'margin_zsl',\n",
       " 'moneyflow_hsgt',\n",
       " 'month_boxoffice',\n",
       " 'new_cbonds',\n",
       " 'new_stocks',\n",
       " 'notice_content',\n",
       " 'os',\n",
       " 'pledged_detail',\n",
       " 'pro',\n",
       " 'pro_api',\n",
       " 'pro_bar',\n",
       " 'profit_data',\n",
       " 'profit_divis',\n",
       " 'quotes',\n",
       " 'realtime_boxoffice',\n",
       " 'realtime_list',\n",
       " 'realtime_quote',\n",
       " 'realtime_tick',\n",
       " 'reset_instrument',\n",
       " 'set_token',\n",
       " 'sh_margin_details',\n",
       " 'sh_margins',\n",
       " 'shibor_data',\n",
       " 'shibor_ma_data',\n",
       " 'shibor_quote_data',\n",
       " 'stock',\n",
       " 'stock_issuance',\n",
       " 'stock_pledged',\n",
       " 'subs',\n",
       " 'sz_margin_details',\n",
       " 'sz_margins',\n",
       " 'tick',\n",
       " 'top10_holders',\n",
       " 'top_list',\n",
       " 'trade_cal',\n",
       " 'trader',\n",
       " 'util',\n",
       " 'xsg_data']"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dir(ts)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Tushare 初始化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tushare as ts\n",
    "\n",
    "# 设置 token 并初始化\n",
    "ts.set_token('31e9665d4e75133893798892cb41e028ebbf8e477c4fdc0205a0ca68')\n",
    "pro = ts.pro_api()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on function pro_api in module tushare.pro.data_pro:\n",
      "\n",
      "pro_api(token='', timeout=30)\n",
      "    初始化pro API,第一次可以通过ts.set_token('your token')来记录自己的token凭证，临时token可以通过本参数传入\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#pro=ts.get_hist_data('603722')\n",
    "help(ts.pro_api)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### A股日线行情\n",
    "\n",
    "接口：daily，可以通过数据工具调试和查看数据   \n",
    "数据说明：交易日每天15点～16点之间入库。本接口是未复权行情，停牌期间不提供数据   \n",
    "调取说明：120积分每分钟内最多调取500次，每次6000条数据，相当于单次提取23年历史   \n",
    "描述：获取股票行情数据，或通过通用行情接口获取数据，包含了前后复权数据   \n",
    "\n",
    "<p><strong>输入参数</strong></p>\n",
    "<table>\n",
    "<thead>\n",
    "<tr>\n",
    "<th>名称</th>\n",
    "<th>类型</th>\n",
    "<th>必选</th>\n",
    "<th>描述</th>\n",
    "</tr>\n",
    "</thead>\n",
    "<tbody><tr>\n",
    "<td>ts_code</td>\n",
    "<td>str</td>\n",
    "<td>N</td>\n",
    "<td>股票代码（支持多个股票同时提取，逗号分隔）</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>trade_date</td>\n",
    "<td>str</td>\n",
    "<td>N</td>\n",
    "<td>交易日期（YYYYMMDD）</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>start_date</td>\n",
    "<td>str</td>\n",
    "<td>N</td>\n",
    "<td>开始日期(YYYYMMDD)</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>end_date</td>\n",
    "<td>str</td>\n",
    "<td>N</td>\n",
    "<td>结束日期(YYYYMMDD)</td>\n",
    "</tr>\n",
    "</tbody>\n",
    "</table>\n",
    "\n",
    "\n",
    "<p><strong>注：日期都填YYYYMMDD格式，比如20181010</strong></p>\n",
    "<p><strong>输出参数</strong></p>\n",
    "<table>\n",
    "<thead>\n",
    "<tr>\n",
    "<th>名称</th>\n",
    "<th>类型</th>\n",
    "<th>描述</th>\n",
    "</tr>\n",
    "</thead>\n",
    "<tbody><tr>\n",
    "<td>ts_code</td>\n",
    "<td>str</td>\n",
    "<td>股票代码</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>trade_date</td>\n",
    "<td>str</td>\n",
    "<td>交易日期</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>open</td>\n",
    "<td>float</td>\n",
    "<td>开盘价</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>high</td>\n",
    "<td>float</td>\n",
    "<td>最高价</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>low</td>\n",
    "<td>float</td>\n",
    "<td>最低价</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>close</td>\n",
    "<td>float</td>\n",
    "<td>收盘价</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>pre_close</td>\n",
    "<td>float</td>\n",
    "<td>昨收价(前复权)</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>change</td>\n",
    "<td>float</td>\n",
    "<td>涨跌额</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>pct_chg</td>\n",
    "<td>float</td>\n",
    "<td>涨跌幅 （未复权，如果是复权请用 <a href=\"https://tushare.pro/document/2?doc_id=109\">通用行情接口</a> ）</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>vol</td>\n",
    "<td>float</td>\n",
    "<td>成交量 （手）</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>amount</td>\n",
    "<td>float</td>\n",
    "<td>成交额 （千元）</td>\n",
    "</tr>\n",
    "</tbody></table>\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "### 初始化pro接口\n",
    "### 在这个网址https://tushare.pro/注册，并在此处获得token，https://tushare.pro/user/token\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        ts_code trade_date   open   high    low  close  pre_close  change  \\\n",
      "0     000031.SZ   20241225   3.26   3.27   3.13   3.18       3.25   -0.07   \n",
      "1     000031.SZ   20241224   3.24   3.27   3.20   3.25       3.25    0.00   \n",
      "2     000031.SZ   20241223   3.37   3.37   3.23   3.25       3.38   -0.13   \n",
      "3     000031.SZ   20241220   3.41   3.46   3.34   3.38       3.36    0.02   \n",
      "4     000031.SZ   20241219   3.43   3.45   3.29   3.36       3.49   -0.13   \n",
      "...         ...        ...    ...    ...    ...    ...        ...     ...   \n",
      "3440  000031.SZ   20100108  10.80  11.06  10.72  10.99      10.90    0.09   \n",
      "3441  000031.SZ   20100107  10.76  11.10  10.60  10.90      10.76    0.14   \n",
      "3442  000031.SZ   20100106  10.69  10.99  10.56  10.76      10.74    0.02   \n",
      "3443  000031.SZ   20100105  10.93  10.93  10.50  10.74      10.97   -0.23   \n",
      "3444  000031.SZ   20100104  11.28  11.35  10.97  10.97      11.23   -0.26   \n",
      "\n",
      "      pct_chg        vol       amount  \n",
      "0     -2.1538  227174.42   72240.1240  \n",
      "1      0.0000  287058.04   92813.4530  \n",
      "2     -3.8462  420661.53  138141.0720  \n",
      "3      0.5952  441125.42  149502.7320  \n",
      "4     -3.7249  574463.41  192491.4790  \n",
      "...       ...        ...          ...  \n",
      "3440   0.8300  199598.97  218361.6184  \n",
      "3441   1.3000  237065.63  257000.1792  \n",
      "3442   0.1900  198426.45  214620.6070  \n",
      "3443  -2.1000  253902.66  271544.2889  \n",
      "3444  -2.3200  243247.60  269690.2107  \n",
      "\n",
      "[3445 rows x 11 columns]\n"
     ]
    }
   ],
   "source": [
    "\n",
    "pro = ts.pro_api('31e9665d4e75133893798892cb41e028ebbf8e477c4fdc0205a0ca68')\n",
    "# 拉取数据\n",
    "df = pro.daily(**{\n",
    "    \"ts_code\": \"000031.SZ\",\n",
    "    \"trade_date\": \"\",\n",
    "    \"start_date\": 20100101,\n",
    "    \"end_date\": 20241225,\n",
    "    \"offset\": \"\",\n",
    "    \"limit\": \"\"\n",
    "}, fields=[\n",
    "    \"ts_code\",\n",
    "    \"trade_date\",\n",
    "    \"open\",\n",
    "    \"high\",\n",
    "    \"low\",\n",
    "    \"close\",\n",
    "    \"pre_close\",\n",
    "    \"change\",\n",
    "    \"pct_chg\",\n",
    "    \"vol\",\n",
    "    \"amount\"\n",
    "])\n",
    "print(df)\n",
    "\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        ts_code trade_date   open   high    low  close  pre_close  change  \\\n",
      "0     000031.SZ   20241225   3.26   3.27   3.13   3.18       3.25   -0.07   \n",
      "1     000031.SZ   20241224   3.24   3.27   3.20   3.25       3.25    0.00   \n",
      "2     000031.SZ   20241223   3.37   3.37   3.23   3.25       3.38   -0.13   \n",
      "3     000031.SZ   20241220   3.41   3.46   3.34   3.38       3.36    0.02   \n",
      "4     000031.SZ   20241219   3.43   3.45   3.29   3.36       3.49   -0.13   \n",
      "...         ...        ...    ...    ...    ...    ...        ...     ...   \n",
      "3440  000031.SZ   20100108  10.80  11.06  10.72  10.99      10.90    0.09   \n",
      "3441  000031.SZ   20100107  10.76  11.10  10.60  10.90      10.76    0.14   \n",
      "3442  000031.SZ   20100106  10.69  10.99  10.56  10.76      10.74    0.02   \n",
      "3443  000031.SZ   20100105  10.93  10.93  10.50  10.74      10.97   -0.23   \n",
      "3444  000031.SZ   20100104  11.28  11.35  10.97  10.97      11.23   -0.26   \n",
      "\n",
      "      pct_chg        vol       amount  \n",
      "0     -2.1538  227174.42   72240.1240  \n",
      "1      0.0000  287058.04   92813.4530  \n",
      "2     -3.8462  420661.53  138141.0720  \n",
      "3      0.5952  441125.42  149502.7320  \n",
      "4     -3.7249  574463.41  192491.4790  \n",
      "...       ...        ...          ...  \n",
      "3440   0.8300  199598.97  218361.6184  \n",
      "3441   1.3000  237065.63  257000.1792  \n",
      "3442   0.1900  198426.45  214620.6070  \n",
      "3443  -2.1000  253902.66  271544.2889  \n",
      "3444  -2.3200  243247.60  269690.2107  \n",
      "\n",
      "[3445 rows x 11 columns]\n"
     ]
    }
   ],
   "source": [
    "### 存储数据\n",
    "print(df)\n",
    "df.to_csv(\"000031.csv\")\n",
    "stockname='大悦城'\n",
    "stockcode='000031'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>...</th>\n",
       "      <th>3435</th>\n",
       "      <th>3436</th>\n",
       "      <th>3437</th>\n",
       "      <th>3438</th>\n",
       "      <th>3439</th>\n",
       "      <th>3440</th>\n",
       "      <th>3441</th>\n",
       "      <th>3442</th>\n",
       "      <th>3443</th>\n",
       "      <th>3444</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ts_code</th>\n",
       "      <td>000031.SZ</td>\n",
       "      <td>000031.SZ</td>\n",
       "      <td>000031.SZ</td>\n",
       "      <td>000031.SZ</td>\n",
       "      <td>000031.SZ</td>\n",
       "      <td>000031.SZ</td>\n",
       "      <td>000031.SZ</td>\n",
       "      <td>000031.SZ</td>\n",
       "      <td>000031.SZ</td>\n",
       "      <td>000031.SZ</td>\n",
       "      <td>...</td>\n",
       "      <td>000031.SZ</td>\n",
       "      <td>000031.SZ</td>\n",
       "      <td>000031.SZ</td>\n",
       "      <td>000031.SZ</td>\n",
       "      <td>000031.SZ</td>\n",
       "      <td>000031.SZ</td>\n",
       "      <td>000031.SZ</td>\n",
       "      <td>000031.SZ</td>\n",
       "      <td>000031.SZ</td>\n",
       "      <td>000031.SZ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>trade_date</th>\n",
       "      <td>20241225</td>\n",
       "      <td>20241224</td>\n",
       "      <td>20241223</td>\n",
       "      <td>20241220</td>\n",
       "      <td>20241219</td>\n",
       "      <td>20241218</td>\n",
       "      <td>20241217</td>\n",
       "      <td>20241216</td>\n",
       "      <td>20241213</td>\n",
       "      <td>20241212</td>\n",
       "      <td>...</td>\n",
       "      <td>20100115</td>\n",
       "      <td>20100114</td>\n",
       "      <td>20100113</td>\n",
       "      <td>20100112</td>\n",
       "      <td>20100111</td>\n",
       "      <td>20100108</td>\n",
       "      <td>20100107</td>\n",
       "      <td>20100106</td>\n",
       "      <td>20100105</td>\n",
       "      <td>20100104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>open</th>\n",
       "      <td>3.26</td>\n",
       "      <td>3.24</td>\n",
       "      <td>3.37</td>\n",
       "      <td>3.41</td>\n",
       "      <td>3.43</td>\n",
       "      <td>3.55</td>\n",
       "      <td>3.87</td>\n",
       "      <td>4.02</td>\n",
       "      <td>3.64</td>\n",
       "      <td>3.54</td>\n",
       "      <td>...</td>\n",
       "      <td>10.55</td>\n",
       "      <td>10.51</td>\n",
       "      <td>10.56</td>\n",
       "      <td>10.77</td>\n",
       "      <td>11.41</td>\n",
       "      <td>10.8</td>\n",
       "      <td>10.76</td>\n",
       "      <td>10.69</td>\n",
       "      <td>10.93</td>\n",
       "      <td>11.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>high</th>\n",
       "      <td>3.27</td>\n",
       "      <td>3.27</td>\n",
       "      <td>3.37</td>\n",
       "      <td>3.46</td>\n",
       "      <td>3.45</td>\n",
       "      <td>3.6</td>\n",
       "      <td>3.89</td>\n",
       "      <td>4.22</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.66</td>\n",
       "      <td>...</td>\n",
       "      <td>10.78</td>\n",
       "      <td>10.65</td>\n",
       "      <td>10.74</td>\n",
       "      <td>11.06</td>\n",
       "      <td>11.5</td>\n",
       "      <td>11.06</td>\n",
       "      <td>11.1</td>\n",
       "      <td>10.99</td>\n",
       "      <td>10.93</td>\n",
       "      <td>11.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>low</th>\n",
       "      <td>3.13</td>\n",
       "      <td>3.2</td>\n",
       "      <td>3.23</td>\n",
       "      <td>3.34</td>\n",
       "      <td>3.29</td>\n",
       "      <td>3.45</td>\n",
       "      <td>3.61</td>\n",
       "      <td>3.98</td>\n",
       "      <td>3.46</td>\n",
       "      <td>3.48</td>\n",
       "      <td>...</td>\n",
       "      <td>10.55</td>\n",
       "      <td>10.33</td>\n",
       "      <td>10.5</td>\n",
       "      <td>10.5</td>\n",
       "      <td>10.74</td>\n",
       "      <td>10.72</td>\n",
       "      <td>10.6</td>\n",
       "      <td>10.56</td>\n",
       "      <td>10.5</td>\n",
       "      <td>10.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>close</th>\n",
       "      <td>3.18</td>\n",
       "      <td>3.25</td>\n",
       "      <td>3.25</td>\n",
       "      <td>3.38</td>\n",
       "      <td>3.36</td>\n",
       "      <td>3.49</td>\n",
       "      <td>3.61</td>\n",
       "      <td>4.01</td>\n",
       "      <td>3.9</td>\n",
       "      <td>3.64</td>\n",
       "      <td>...</td>\n",
       "      <td>10.67</td>\n",
       "      <td>10.56</td>\n",
       "      <td>10.52</td>\n",
       "      <td>10.9</td>\n",
       "      <td>10.79</td>\n",
       "      <td>10.99</td>\n",
       "      <td>10.9</td>\n",
       "      <td>10.76</td>\n",
       "      <td>10.74</td>\n",
       "      <td>10.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pre_close</th>\n",
       "      <td>3.25</td>\n",
       "      <td>3.25</td>\n",
       "      <td>3.38</td>\n",
       "      <td>3.36</td>\n",
       "      <td>3.49</td>\n",
       "      <td>3.61</td>\n",
       "      <td>4.01</td>\n",
       "      <td>3.9</td>\n",
       "      <td>3.64</td>\n",
       "      <td>3.54</td>\n",
       "      <td>...</td>\n",
       "      <td>10.56</td>\n",
       "      <td>10.52</td>\n",
       "      <td>10.9</td>\n",
       "      <td>10.79</td>\n",
       "      <td>10.99</td>\n",
       "      <td>10.9</td>\n",
       "      <td>10.76</td>\n",
       "      <td>10.74</td>\n",
       "      <td>10.97</td>\n",
       "      <td>11.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>change</th>\n",
       "      <td>-0.07</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.13</td>\n",
       "      <td>0.02</td>\n",
       "      <td>-0.13</td>\n",
       "      <td>-0.12</td>\n",
       "      <td>-0.4</td>\n",
       "      <td>0.11</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.1</td>\n",
       "      <td>...</td>\n",
       "      <td>0.11</td>\n",
       "      <td>0.04</td>\n",
       "      <td>-0.38</td>\n",
       "      <td>0.11</td>\n",
       "      <td>-0.2</td>\n",
       "      <td>0.09</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0.02</td>\n",
       "      <td>-0.23</td>\n",
       "      <td>-0.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pct_chg</th>\n",
       "      <td>-2.1538</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-3.8462</td>\n",
       "      <td>0.5952</td>\n",
       "      <td>-3.7249</td>\n",
       "      <td>-3.3241</td>\n",
       "      <td>-9.9751</td>\n",
       "      <td>2.8205</td>\n",
       "      <td>7.1429</td>\n",
       "      <td>2.8249</td>\n",
       "      <td>...</td>\n",
       "      <td>1.04</td>\n",
       "      <td>0.38</td>\n",
       "      <td>-3.49</td>\n",
       "      <td>1.02</td>\n",
       "      <td>-1.82</td>\n",
       "      <td>0.83</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.19</td>\n",
       "      <td>-2.1</td>\n",
       "      <td>-2.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vol</th>\n",
       "      <td>227174.42</td>\n",
       "      <td>287058.04</td>\n",
       "      <td>420661.53</td>\n",
       "      <td>441125.42</td>\n",
       "      <td>574463.41</td>\n",
       "      <td>748356.57</td>\n",
       "      <td>1140372.24</td>\n",
       "      <td>2354547.32</td>\n",
       "      <td>1781899.65</td>\n",
       "      <td>632274.92</td>\n",
       "      <td>...</td>\n",
       "      <td>185788.67</td>\n",
       "      <td>192033.73</td>\n",
       "      <td>213002.84</td>\n",
       "      <td>222285.05</td>\n",
       "      <td>272699.92</td>\n",
       "      <td>199598.97</td>\n",
       "      <td>237065.63</td>\n",
       "      <td>198426.45</td>\n",
       "      <td>253902.66</td>\n",
       "      <td>243247.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>amount</th>\n",
       "      <td>72240.124</td>\n",
       "      <td>92813.453</td>\n",
       "      <td>138141.072</td>\n",
       "      <td>149502.732</td>\n",
       "      <td>192491.479</td>\n",
       "      <td>263459.693</td>\n",
       "      <td>417489.223</td>\n",
       "      <td>961656.246</td>\n",
       "      <td>681208.602</td>\n",
       "      <td>227145.346</td>\n",
       "      <td>...</td>\n",
       "      <td>198759.3619</td>\n",
       "      <td>201687.5133</td>\n",
       "      <td>225730.2175</td>\n",
       "      <td>239201.1372</td>\n",
       "      <td>301312.3082</td>\n",
       "      <td>218361.6184</td>\n",
       "      <td>257000.1792</td>\n",
       "      <td>214620.607</td>\n",
       "      <td>271544.2889</td>\n",
       "      <td>269690.2107</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>11 rows × 3445 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 0          1           2           3           4     \\\n",
       "ts_code     000031.SZ  000031.SZ   000031.SZ   000031.SZ   000031.SZ   \n",
       "trade_date   20241225   20241224    20241223    20241220    20241219   \n",
       "open             3.26       3.24        3.37        3.41        3.43   \n",
       "high             3.27       3.27        3.37        3.46        3.45   \n",
       "low              3.13        3.2        3.23        3.34        3.29   \n",
       "close            3.18       3.25        3.25        3.38        3.36   \n",
       "pre_close        3.25       3.25        3.38        3.36        3.49   \n",
       "change          -0.07        0.0       -0.13        0.02       -0.13   \n",
       "pct_chg       -2.1538        0.0     -3.8462      0.5952     -3.7249   \n",
       "vol         227174.42  287058.04   420661.53   441125.42   574463.41   \n",
       "amount      72240.124  92813.453  138141.072  149502.732  192491.479   \n",
       "\n",
       "                  5           6           7           8           9     ...  \\\n",
       "ts_code      000031.SZ   000031.SZ   000031.SZ   000031.SZ   000031.SZ  ...   \n",
       "trade_date    20241218    20241217    20241216    20241213    20241212  ...   \n",
       "open              3.55        3.87        4.02        3.64        3.54  ...   \n",
       "high               3.6        3.89        4.22         4.0        3.66  ...   \n",
       "low               3.45        3.61        3.98        3.46        3.48  ...   \n",
       "close             3.49        3.61        4.01         3.9        3.64  ...   \n",
       "pre_close         3.61        4.01         3.9        3.64        3.54  ...   \n",
       "change           -0.12        -0.4        0.11        0.26         0.1  ...   \n",
       "pct_chg        -3.3241     -9.9751      2.8205      7.1429      2.8249  ...   \n",
       "vol          748356.57  1140372.24  2354547.32  1781899.65   632274.92  ...   \n",
       "amount      263459.693  417489.223  961656.246  681208.602  227145.346  ...   \n",
       "\n",
       "                   3435         3436         3437         3438         3439  \\\n",
       "ts_code       000031.SZ    000031.SZ    000031.SZ    000031.SZ    000031.SZ   \n",
       "trade_date     20100115     20100114     20100113     20100112     20100111   \n",
       "open              10.55        10.51        10.56        10.77        11.41   \n",
       "high              10.78        10.65        10.74        11.06         11.5   \n",
       "low               10.55        10.33         10.5         10.5        10.74   \n",
       "close             10.67        10.56        10.52         10.9        10.79   \n",
       "pre_close         10.56        10.52         10.9        10.79        10.99   \n",
       "change             0.11         0.04        -0.38         0.11         -0.2   \n",
       "pct_chg            1.04         0.38        -3.49         1.02        -1.82   \n",
       "vol           185788.67    192033.73    213002.84    222285.05    272699.92   \n",
       "amount      198759.3619  201687.5133  225730.2175  239201.1372  301312.3082   \n",
       "\n",
       "                   3440         3441        3442         3443         3444  \n",
       "ts_code       000031.SZ    000031.SZ   000031.SZ    000031.SZ    000031.SZ  \n",
       "trade_date     20100108     20100107    20100106     20100105     20100104  \n",
       "open               10.8        10.76       10.69        10.93        11.28  \n",
       "high              11.06         11.1       10.99        10.93        11.35  \n",
       "low               10.72         10.6       10.56         10.5        10.97  \n",
       "close             10.99         10.9       10.76        10.74        10.97  \n",
       "pre_close          10.9        10.76       10.74        10.97        11.23  \n",
       "change             0.09         0.14        0.02        -0.23        -0.26  \n",
       "pct_chg            0.83          1.3        0.19         -2.1        -2.32  \n",
       "vol           199598.97    237065.63   198426.45    253902.66     243247.6  \n",
       "amount      218361.6184  257000.1792  214620.607  271544.2889  269690.2107  \n",
       "\n",
       "[11 rows x 3445 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#数据转置\n",
    "df.T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#输出图形\n",
    "df[\"close\"].plot(figsize=(16,4),legend=True)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x259c7f2acf0>]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#读取数据\n",
    "#coding=utf-8\n",
    "#使用pandas读取数据\n",
    "mydata=pd.read_csv(\"./000031.csv\",parse_dates=[\"trade_date\"],index_col=\"trade_date\")[[\"open\",\"high\",\"low\",\"close\"]]\n",
    "\n",
    "# data=pd.read_csv(\"./stock688333.csv\",parse_dates=[\"trade_date\"])[[\"trade_date\",\"open\",\"high\",\"low\",\"close\",\"vol\"]]\n",
    "\n",
    "#翻转数据\n",
    "\n",
    "mydata=mydata.iloc[::-1]\n",
    "\n",
    "plt.plot(mydata[\"close\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 3415 entries, 0 to 3414\n",
      "Data columns (total 5 columns):\n",
      " #   Column      Non-Null Count  Dtype         \n",
      "---  ------      --------------  -----         \n",
      " 0   trade_date  3415 non-null   datetime64[ns]\n",
      " 1   open        3415 non-null   float64       \n",
      " 2   high        3415 non-null   float64       \n",
      " 3   low         3415 non-null   float64       \n",
      " 4   close       3415 non-null   float64       \n",
      "dtypes: datetime64[ns](1), float64(4)\n",
      "memory usage: 133.5 KB\n",
      "None\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>trade_date</th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>5</th>\n",
       "      <th>10</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3410</th>\n",
       "      <td>2024-12-19</td>\n",
       "      <td>3.43</td>\n",
       "      <td>3.45</td>\n",
       "      <td>3.29</td>\n",
       "      <td>3.36</td>\n",
       "      <td>3.674</td>\n",
       "      <td>3.570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3411</th>\n",
       "      <td>2024-12-20</td>\n",
       "      <td>3.41</td>\n",
       "      <td>3.46</td>\n",
       "      <td>3.34</td>\n",
       "      <td>3.38</td>\n",
       "      <td>3.570</td>\n",
       "      <td>3.566</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3412</th>\n",
       "      <td>2024-12-23</td>\n",
       "      <td>3.37</td>\n",
       "      <td>3.37</td>\n",
       "      <td>3.23</td>\n",
       "      <td>3.25</td>\n",
       "      <td>3.418</td>\n",
       "      <td>3.559</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3413</th>\n",
       "      <td>2024-12-24</td>\n",
       "      <td>3.24</td>\n",
       "      <td>3.27</td>\n",
       "      <td>3.20</td>\n",
       "      <td>3.25</td>\n",
       "      <td>3.346</td>\n",
       "      <td>3.543</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3414</th>\n",
       "      <td>2024-12-25</td>\n",
       "      <td>3.26</td>\n",
       "      <td>3.27</td>\n",
       "      <td>3.13</td>\n",
       "      <td>3.18</td>\n",
       "      <td>3.284</td>\n",
       "      <td>3.507</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     trade_date  open  high   low  close      5     10\n",
       "3410 2024-12-19  3.43  3.45  3.29   3.36  3.674  3.570\n",
       "3411 2024-12-20  3.41  3.46  3.34   3.38  3.570  3.566\n",
       "3412 2024-12-23  3.37  3.37  3.23   3.25  3.418  3.559\n",
       "3413 2024-12-24  3.24  3.27  3.20   3.25  3.346  3.543\n",
       "3414 2024-12-25  3.26  3.27  3.13   3.18  3.284  3.507"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 获取股价数据\n",
    "# import akshare as ak\n",
    "\n",
    "df3 = mydata.reset_index().iloc[30:, :6]  # 取过去30天数据\n",
    "df3 = df3.dropna(how='any').reset_index(drop=True) #去除空值且从零开始编号索引\n",
    "df3 = df3.sort_values(by='trade_date', ascending=True)\n",
    "print(df3.info())\n",
    "\n",
    "# 计算均线数据\n",
    "df3['5'] = df3.close.rolling(5).mean()\n",
    "df3['10'] = df3.close.rolling(10).mean()\n",
    "\n",
    "df3.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib\n",
    "matplotlib.style.use('ggplot') #用于调整图标样式，可选\n",
    "\n",
    "import mplfinance as mpf\n",
    "from mplfinance.original_flavor import candlestick2_ohlc\n",
    "from matplotlib.ticker import FormatStrFormatter\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "fig, ax = plt.subplots(1, 1, figsize=(8,3), dpi=200)\n",
    "\n",
    "candlestick2_ohlc(ax,\n",
    "                opens = df3[ 'open'].values,\n",
    "                highs = df3['high'].values,\n",
    "                lows = df3[ 'low'].values,\n",
    "                closes = df3['close'].values,\n",
    "                width=0.5, colorup=\"r\",colordown=\"g\")\n",
    "\n",
    "# 显示最高点和最低点\n",
    "ax.text( df3.high.idxmax(), df3.high.max(),   s =df3.high.max(), fontsize=8)\n",
    "ax.text( df3.high.idxmin(), df3.high.min()-2, s = df3.high.min(), fontsize=8)\n",
    "\n",
    "ax.set_facecolor(\"white\")\n",
    "ax.set_title(stockname, fontsize=8)\n",
    "\n",
    "# 画均线\n",
    "plt.plot(df3['5'].values, alpha = 0.5, label='MA5')\n",
    "plt.plot(df3['10'].values, alpha = 0.5, label='MA10')\n",
    "\n",
    "ax.legend(facecolor='white', edgecolor='white', fontsize=6)\n",
    "\n",
    "# 修改x轴坐标\n",
    "plt.xticks(ticks =  np.arange(0,len(df3)), labels = df3.trade_date.dt.strftime('%Y-%m-%d').to_numpy() )\n",
    "plt.xticks(rotation=45, size=8)\n",
    "\n",
    "# 修改y轴坐标\n",
    "ax.yaxis.set_major_formatter(FormatStrFormatter('%.2f'))\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "#from matplotlib.dates  import date2num\n",
    "#data['date']=date2num(data.index.to_pydatetime())\n",
    "#data=data.sort_values(by=\"date\",ascending=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     trade_date   open   high    low  close\n",
      "0    2010-01-04  11.28  11.35  10.97  10.97\n",
      "1    2010-01-05  10.93  10.93  10.50  10.74\n",
      "2    2010-01-06  10.69  10.99  10.56  10.76\n",
      "3    2010-01-07  10.76  11.10  10.60  10.90\n",
      "4    2010-01-08  10.80  11.06  10.72  10.99\n",
      "...         ...    ...    ...    ...    ...\n",
      "3440 2024-12-19   3.43   3.45   3.29   3.36\n",
      "3441 2024-12-20   3.41   3.46   3.34   3.38\n",
      "3442 2024-12-23   3.37   3.37   3.23   3.25\n",
      "3443 2024-12-24   3.24   3.27   3.20   3.25\n",
      "3444 2024-12-25   3.26   3.27   3.13   3.18\n",
      "\n",
      "[3445 rows x 5 columns]\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 3445 entries, 0 to 3444\n",
      "Data columns (total 5 columns):\n",
      " #   Column      Non-Null Count  Dtype         \n",
      "---  ------      --------------  -----         \n",
      " 0   trade_date  3445 non-null   datetime64[ns]\n",
      " 1   open        3445 non-null   float64       \n",
      " 2   high        3445 non-null   float64       \n",
      " 3   low         3445 non-null   float64       \n",
      " 4   close       3445 non-null   float64       \n",
      "dtypes: datetime64[ns](1), float64(4)\n",
      "memory usage: 134.7 KB\n",
      "None\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>trade_date</th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>5</th>\n",
       "      <th>10</th>\n",
       "      <th>30</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2010-01-04</td>\n",
       "      <td>11.28</td>\n",
       "      <td>11.35</td>\n",
       "      <td>10.97</td>\n",
       "      <td>10.97</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2010-01-05</td>\n",
       "      <td>10.93</td>\n",
       "      <td>10.93</td>\n",
       "      <td>10.50</td>\n",
       "      <td>10.74</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2010-01-06</td>\n",
       "      <td>10.69</td>\n",
       "      <td>10.99</td>\n",
       "      <td>10.56</td>\n",
       "      <td>10.76</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2010-01-07</td>\n",
       "      <td>10.76</td>\n",
       "      <td>11.10</td>\n",
       "      <td>10.60</td>\n",
       "      <td>10.90</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2010-01-08</td>\n",
       "      <td>10.80</td>\n",
       "      <td>11.06</td>\n",
       "      <td>10.72</td>\n",
       "      <td>10.99</td>\n",
       "      <td>10.872</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3440</th>\n",
       "      <td>2024-12-19</td>\n",
       "      <td>3.43</td>\n",
       "      <td>3.45</td>\n",
       "      <td>3.29</td>\n",
       "      <td>3.36</td>\n",
       "      <td>3.674</td>\n",
       "      <td>3.570</td>\n",
       "      <td>3.347333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3441</th>\n",
       "      <td>2024-12-20</td>\n",
       "      <td>3.41</td>\n",
       "      <td>3.46</td>\n",
       "      <td>3.34</td>\n",
       "      <td>3.38</td>\n",
       "      <td>3.570</td>\n",
       "      <td>3.566</td>\n",
       "      <td>3.346333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3442</th>\n",
       "      <td>2024-12-23</td>\n",
       "      <td>3.37</td>\n",
       "      <td>3.37</td>\n",
       "      <td>3.23</td>\n",
       "      <td>3.25</td>\n",
       "      <td>3.418</td>\n",
       "      <td>3.559</td>\n",
       "      <td>3.343667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3443</th>\n",
       "      <td>2024-12-24</td>\n",
       "      <td>3.24</td>\n",
       "      <td>3.27</td>\n",
       "      <td>3.20</td>\n",
       "      <td>3.25</td>\n",
       "      <td>3.346</td>\n",
       "      <td>3.543</td>\n",
       "      <td>3.341667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3444</th>\n",
       "      <td>2024-12-25</td>\n",
       "      <td>3.26</td>\n",
       "      <td>3.27</td>\n",
       "      <td>3.13</td>\n",
       "      <td>3.18</td>\n",
       "      <td>3.284</td>\n",
       "      <td>3.507</td>\n",
       "      <td>3.338667</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3445 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     trade_date   open   high    low  close       5     10        30\n",
       "0    2010-01-04  11.28  11.35  10.97  10.97     NaN    NaN       NaN\n",
       "1    2010-01-05  10.93  10.93  10.50  10.74     NaN    NaN       NaN\n",
       "2    2010-01-06  10.69  10.99  10.56  10.76     NaN    NaN       NaN\n",
       "3    2010-01-07  10.76  11.10  10.60  10.90     NaN    NaN       NaN\n",
       "4    2010-01-08  10.80  11.06  10.72  10.99  10.872    NaN       NaN\n",
       "...         ...    ...    ...    ...    ...     ...    ...       ...\n",
       "3440 2024-12-19   3.43   3.45   3.29   3.36   3.674  3.570  3.347333\n",
       "3441 2024-12-20   3.41   3.46   3.34   3.38   3.570  3.566  3.346333\n",
       "3442 2024-12-23   3.37   3.37   3.23   3.25   3.418  3.559  3.343667\n",
       "3443 2024-12-24   3.24   3.27   3.20   3.25   3.346  3.543  3.341667\n",
       "3444 2024-12-25   3.26   3.27   3.13   3.18   3.284  3.507  3.338667\n",
       "\n",
       "[3445 rows x 8 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 获取股价数据\n",
    "df3 = mydata.reset_index().iloc[:,:]  #取过去30天数据\n",
    "df3 = df3.dropna(how='any').reset_index(drop=True) #去除空值且从零开始编号索引\n",
    "print(df3)\n",
    "df3 = df3.sort_values(by='trade_date', ascending=True)\n",
    "print(df3.info())\n",
    "\n",
    "# 均线数据\n",
    "df3['5'] = df3.close.rolling(5).mean()\n",
    "df3['10'] = df3.close.rolling(10).mean()\n",
    "df3['30']=df3.close.rolling(30).mean()\n",
    "\n",
    "df3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "matplotlib.style.use('ggplot') #用于调整图标样式，可选\n",
    "from mplfinance.original_flavor import candlestick2_ohlc\n",
    "from matplotlib.ticker import FormatStrFormatter\n",
    "\n",
    "fig, ax = plt.subplots(1, 1, figsize=(8,3), dpi=200)\n",
    "\n",
    "candlestick2_ohlc(ax,\n",
    "                opens = df3[ 'open'].values,\n",
    "                highs = df3['high'].values,\n",
    "                lows = df3[ 'low'].values,\n",
    "                closes = df3['close'].values,\n",
    "                width=0.5, colorup=\"r\",colordown=\"g\")\n",
    "\n",
    "# 显示最高点和最低点\n",
    "ax.text( df3.high.idxmax(), df3.high.max(),   s =df3.high.max(), fontsize=8)\n",
    "ax.text( df3.high.idxmin(), df3.high.min()-2, s = df3.high.min(), fontsize=8)\n",
    "\n",
    "ax.set_facecolor(\"white\")\n",
    "ax.set_title(stockname+\"股价走势\", fontsize=8)\n",
    "\n",
    "# 画均线\n",
    "plt.plot(df3['5'].values, alpha = 0.5, label='MA5')\n",
    "plt.plot(df3['10'].values, alpha = 0.5, label='MA10')\n",
    "plt.plot(df3['30'].values,alpha=0.5, label='MA30')\n",
    "\n",
    "ax.legend(facecolor='white', edgecolor='white', fontsize=6)\n",
    "\n",
    "# 修改x轴坐标\n",
    "tempXticks=np.arange(0,len(df3))\n",
    "#XticksData=data.asfreq(\"2M\").dropna()\n",
    "nameXticks =  df3.trade_date.dt.strftime('%Y-%m-%d').to_numpy()\n",
    "\n",
    "plt.xticks(ticks =tempXticks , labels =nameXticks )\n",
    "plt.xticks(rotation=45, size=8)\n",
    "#修改X轴间隔\n",
    "x_major_locator=plt.MultipleLocator(10)\n",
    "ax.xaxis.set_major_locator(x_major_locator)\n",
    "ax.spines['bottom'].set_color('red')\n",
    "ax.spines['left'].set_color('red')\n",
    "plt.xlim(0,100)\n",
    "# 修改y轴坐标\n",
    "ax.yaxis.set_major_formatter(FormatStrFormatter('%.2f'))\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# data1=pd.DataFrame(data,columns=[\"trade_date\",\"close\"])\n",
    "#data1=pd.DataFrame(data,columns=[\"close\"])\n",
    "data1=mydata\n",
    "data1[\"open\"].plot(label=\"open\")\n",
    "data1[\"close\"].plot(label=\"close\")\n",
    "plt.legend()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method DataFrame.items of       trade_date  close\n",
       "0           3.26   3.27\n",
       "1           3.24   3.27\n",
       "2           3.37   3.37\n",
       "3           3.41   3.46\n",
       "4           3.43   3.45\n",
       "...          ...    ...\n",
       "3440       10.80  11.06\n",
       "3441       10.76  11.10\n",
       "3442       10.69  10.99\n",
       "3443       10.93  10.93\n",
       "3444       11.28  11.35\n",
       "\n",
       "[3445 rows x 2 columns]>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ddd=[]\n",
    "for item in data1.items():\n",
    "    ddd.append(item)\n",
    "ind=ddd[0][1].values\n",
    "da1=ddd[1][1].values\n",
    "index1=[]\n",
    "data2=[]\n",
    "for item in ind:\n",
    "    index1.append(item)\n",
    "index1.reverse()\n",
    "for item in da1:\n",
    "    data2.append(item)\n",
    "data2.reverse()\n",
    "data1={'trade_date':index1,'close':data2}\n",
    "stockdata=pd.DataFrame(data1)\n",
    "\n",
    "stockdata.items    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[10.97],\n",
       "       [10.74],\n",
       "       [10.76],\n",
       "       ...,\n",
       "       [ 3.25],\n",
       "       [ 3.25],\n",
       "       [ 3.18]])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#数据检查\n",
    "df3.iloc[:,4:5].values[:,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib.dates  import date2num\n",
    "date2num(df3.trade_date.values)\n",
    "#添加data数据列\n",
    "df3['date']=date2num(df3.trade_date.values)\n",
    "#df3=df3.sort_values(by=\"date\",ascending=True)\n",
    "#df3=df3[['date','open', 'high', 'low', 'close']]\n",
    "\n",
    "ind=np.arange(0,2677)\n",
    "dataf1=df3[['date','open', 'high', 'low', 'close']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>14613.0</th>\n",
       "      <td>11.28</td>\n",
       "      <td>11.35</td>\n",
       "      <td>10.97</td>\n",
       "      <td>10.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14614.0</th>\n",
       "      <td>10.93</td>\n",
       "      <td>10.93</td>\n",
       "      <td>10.50</td>\n",
       "      <td>10.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14615.0</th>\n",
       "      <td>10.69</td>\n",
       "      <td>10.99</td>\n",
       "      <td>10.56</td>\n",
       "      <td>10.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14616.0</th>\n",
       "      <td>10.76</td>\n",
       "      <td>11.10</td>\n",
       "      <td>10.60</td>\n",
       "      <td>10.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14617.0</th>\n",
       "      <td>10.80</td>\n",
       "      <td>11.06</td>\n",
       "      <td>10.72</td>\n",
       "      <td>10.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20076.0</th>\n",
       "      <td>3.43</td>\n",
       "      <td>3.45</td>\n",
       "      <td>3.29</td>\n",
       "      <td>3.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20077.0</th>\n",
       "      <td>3.41</td>\n",
       "      <td>3.46</td>\n",
       "      <td>3.34</td>\n",
       "      <td>3.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20080.0</th>\n",
       "      <td>3.37</td>\n",
       "      <td>3.37</td>\n",
       "      <td>3.23</td>\n",
       "      <td>3.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20081.0</th>\n",
       "      <td>3.24</td>\n",
       "      <td>3.27</td>\n",
       "      <td>3.20</td>\n",
       "      <td>3.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20082.0</th>\n",
       "      <td>3.26</td>\n",
       "      <td>3.27</td>\n",
       "      <td>3.13</td>\n",
       "      <td>3.18</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3445 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          open   high    low  close\n",
       "date                               \n",
       "14613.0  11.28  11.35  10.97  10.97\n",
       "14614.0  10.93  10.93  10.50  10.74\n",
       "14615.0  10.69  10.99  10.56  10.76\n",
       "14616.0  10.76  11.10  10.60  10.90\n",
       "14617.0  10.80  11.06  10.72  10.99\n",
       "...        ...    ...    ...    ...\n",
       "20076.0   3.43   3.45   3.29   3.36\n",
       "20077.0   3.41   3.46   3.34   3.38\n",
       "20080.0   3.37   3.37   3.23   3.25\n",
       "20081.0   3.24   3.27   3.20   3.25\n",
       "20082.0   3.26   3.27   3.13   3.18\n",
       "\n",
       "[3445 rows x 4 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataf1.set_index(\"date\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 深度学习拟合股票"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 确保结果尽可能重现\n",
    "\n",
    "seed(1)\n",
    "tf.random.set_seed(1)\n",
    "\n",
    "# 设置相关参数\n",
    "n_timestamp  = 5    # 时间戳\n",
    "n_epochs     = 30    # 训练轮数\n",
    "# ====================================\n",
    "#      选择模型：\n",
    "#            1: 单层 LSTM\n",
    "#            2: 多层 LSTM\n",
    "#            3: 双向 LSTM\n",
    "# ====================================\n",
    "model_type = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>14613.0</td>\n",
       "      <td>11.28</td>\n",
       "      <td>11.35</td>\n",
       "      <td>10.97</td>\n",
       "      <td>10.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>14614.0</td>\n",
       "      <td>10.93</td>\n",
       "      <td>10.93</td>\n",
       "      <td>10.50</td>\n",
       "      <td>10.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>14615.0</td>\n",
       "      <td>10.69</td>\n",
       "      <td>10.99</td>\n",
       "      <td>10.56</td>\n",
       "      <td>10.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>14616.0</td>\n",
       "      <td>10.76</td>\n",
       "      <td>11.10</td>\n",
       "      <td>10.60</td>\n",
       "      <td>10.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>14617.0</td>\n",
       "      <td>10.80</td>\n",
       "      <td>11.06</td>\n",
       "      <td>10.72</td>\n",
       "      <td>10.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3440</th>\n",
       "      <td>20076.0</td>\n",
       "      <td>3.43</td>\n",
       "      <td>3.45</td>\n",
       "      <td>3.29</td>\n",
       "      <td>3.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3441</th>\n",
       "      <td>20077.0</td>\n",
       "      <td>3.41</td>\n",
       "      <td>3.46</td>\n",
       "      <td>3.34</td>\n",
       "      <td>3.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3442</th>\n",
       "      <td>20080.0</td>\n",
       "      <td>3.37</td>\n",
       "      <td>3.37</td>\n",
       "      <td>3.23</td>\n",
       "      <td>3.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3443</th>\n",
       "      <td>20081.0</td>\n",
       "      <td>3.24</td>\n",
       "      <td>3.27</td>\n",
       "      <td>3.20</td>\n",
       "      <td>3.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3444</th>\n",
       "      <td>20082.0</td>\n",
       "      <td>3.26</td>\n",
       "      <td>3.27</td>\n",
       "      <td>3.13</td>\n",
       "      <td>3.18</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3445 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         date   open   high    low  close\n",
       "0     14613.0  11.28  11.35  10.97  10.97\n",
       "1     14614.0  10.93  10.93  10.50  10.74\n",
       "2     14615.0  10.69  10.99  10.56  10.76\n",
       "3     14616.0  10.76  11.10  10.60  10.90\n",
       "4     14617.0  10.80  11.06  10.72  10.99\n",
       "...       ...    ...    ...    ...    ...\n",
       "3440  20076.0   3.43   3.45   3.29   3.36\n",
       "3441  20077.0   3.41   3.46   3.34   3.38\n",
       "3442  20080.0   3.37   3.37   3.23   3.25\n",
       "3443  20081.0   3.24   3.27   3.20   3.25\n",
       "3444  20082.0   3.26   3.27   3.13   3.18\n",
       "\n",
       "[3445 rows x 5 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataf1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 拟合开始，数据准备"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x259cc98b770>]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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R8llHjx6FlBK9e/eOsshERCkuqpYUk/uIUkRUY1KmTZuG733ve0HPNzc347XXXsO4cePCPmv37t1QVRXTp0/HDTfcgCeeeAKnTp2KpjhERKnJJEiRUoYfcxIujwpRkomqJSVc68i7776Lc845B4WFhWGfVV1djb59++KGG26AEALLli3Diy++iKlTpwa9x+l0wul0eveFEMjKyvJux4rnWbF8JsUf6y05sd4CCSkhAQi73TtfRwBhgxQh1Xb9ObLuklMy1VvUA2dD2bRpE6699tqIrh03bpyhxeW6667DwoULQwYpGzZswPr16737JSUlKC8vR0FBQavLHEpRUVFcnkvxxXpLTqw3n/pOuTgBICM7G43uY8VFPXDIbofT71q73Y4W93ZGejoKiovbrZwerLvklAz1FrMgpaamBjU1NRg8eHCr7s/OzkZ9fT2cTifS0syTE40fPx5jx4717nuiwNraWrS0tJje0xpCCBQVFaGmpoYD0ZII6y05sd4CqcePAwCadJ9r1QcPwtXUFHCt/rOvqaER1dXV8S+gG+suOVmh3ux2e0QNDDELUt577z0MHTrUMIg2lIULF+Kqq65C//79AQAVFRXIz88PGqAAQFpaWtDz8fhBS08/MCUV1ltyYr35SHeeFKnYdcdUmCZr0x2SqishP0PWXXJKhnqLWTK3Tz/9FOedd17AcYfDYdrK0adPH6xevRq7du3CRx99hJdeegllZWWxKg4RUfJyf2YK/R9lqjSfgqz/JWPxXzhE0YpJkNLc3Ixdu3Z5W0X0Zs2aZUjY5jFu3Dj07NkT8+bNw3PPPYeysjKMHz8+FsUhIkpuLnfGWf26PFINP3uHU5ApxbSqu2fdunWG/fT0dKxZs8b02iVLlpi/sN2OGTNmYMaMGa0pAhFR6nK5W5/T0n3HOAWZOiCu3UNEZDWeLvI0/5aUMEFKiJYUKSXUVU9C/dOqGBSQqH0wSCEishpPS4qhu0cGtpTkdzXuhwpiaqsh33sL8s0N3oG5RFbHIIWIyGrMuntMBs6KCTcb7wvZ0qJL3FXxRdvKR9ROGKQQEVmNd+Csbtig2cBZxQbDHORQA2d1z1KffLDNRSRqDwxSiIispsWdV9Zu961ybDJwVijCbwpyiCBFf2tTY9DLiKyEQQoRkdV4Bs7abIDi7qYxGzgr/D7CQ7WkcOYPJSEGKUREFiM93T02O+BZBE41GTir+C0QF2pMChO9URJikEJEZDWeFhHFZuzu8c84K2zm95nRBzj9Bra9jETtgEEKEZHVeAIKIXwtKWbdPQEtKSGCFH0AYw++RhqRlTBIISKyGm9LiuI3cNYvCAkYkxJhdw/zpFCSYJBCRGQ1noBCKL70Jm1uSdHd62KQQsmBQQoRkdUYunvcH9NmA2fhNwU50jEpFZ9DVnwO2eiISXGJ4oVBChGR1XgCD0U/JsVk4GzAfaGCFOO96oJfQL2fC7yStTFIISKyGn13j7dLx2QVZOHX3RMqiDFrZTl5vNVFJGoPDFKIiKzGbOCsaXePn5AtKUzmRsmHQQoRkdW4Awqhn4Lc0gw0NoS+L+SYFCZzo+TDIIWIyGpU/ewe98f08SPm1xrW7omyu4fI4hikEBFZjUkyN3n6tHYsK8d3XcCYFLakUGphkEJEZDXe2T26gbP6lZE9/IOUSFpSCs+ITRmJ2gGDFCIiq/EEFPruHs/KyGkhUtpH0pKi8GOfkgf/txIRWY1ZnhRvS4o+SIki46y+C4koSTBIISKyGrMFBk2DFD8RtqSIm+7UtrNz21ZOojhjkEJEZDWqrmvG293jDlJsNt91AmFn90jPMV3gI84+N+j1RFbCIIWIyGqkbkyKZ+Cs0xOk6AbO+nf3+LWkyMq9UH92A9S3/6ob5yJ8z2SCN7I4BilERFajmiwwaNqSIgDoW1KMQYf6/GLgVB3ki8t13T023zPZkkIWxyCFiMhq9Gv3+I9JMbSkmN2qCzyam3zb3gRxwjfDhy0pZHEMUoiIrEY/XTggSNG1pBT0CLxX3+XjcumeaTIYN9yqykQJFjokJyKi9idNunuc7jwpNjuU3ywBHKch8rsFudcdyLhafMddvvvZ3UPJgkEKEZHVSH3XjDstvsvXkiKKe+mu9btX35KiC0Kkw5NWP9vXksLuHrI4dvcQEVmNJ9DQT0H2dN2IMB/b+tYRRdc15F5BWWRl62b3sCWFrI1BChGR1ZiNH/EEKX5p7cWI0cZ79S0p+nV+GvQtKb7uHslAhSyMQQoRkYXI/RXAsSPajn7grHtMifAPUq6+HmLKPboH6IIU3bVyX4W2kZ1rTI0vVUjVBbl3F2SLbgwLkQVEHaTU19fjtttuw+HDh73HVq5ciWuvvdb774477ojoWTt37sTdd9+NW265BX/961+jLQoRUcpRH73PtyMUX6DhCSD8g5S0NIjhF/kOBGsZ2b5V+5qXb+wyUiXkG69AnX8P5Lrft63wRDEW1cDZuro6lJeXo7a21nD8q6++wuzZszFgwAAAgBLBKpueZ1111VUYNWoUnnjiCZx11lkYNGhQNEUiIkotTQ2+bXtaQEuKYZyJhyHoCDMYNjfPryVFQr68Wtv859+BSdNbUWii+IiqJeXJJ5/EqFGjDMdcLhcOHDiA0tJS5OTkICcnB1lZWWGftWXLFnTp0gXXXHMNiouLMWHCBLz99tvRlZ6IKJXZ7WHHpACA0AUd8m/rQj5SpGf4Bs4CkB+92/ZyEsVJVC0p06ZNQ48ePfDcc895j+3btw9SSsyaNQvHjh1DaWkpbr31VnTv3j3ks/bt24dBgwZ531z9+vXDmjVrQt7jdDrh9KxfAe2N6QmIRAyXH/c8K5bPpPhjvSUn1ltwIi09cHaPooT8Wcm3NkJMnBb8oXY7hK41Rq58wni+qREiM/wfmgDrLlklU71FFaT06BGY3bCqqgq9evXCzTffjLy8PKxcuRLLly/HfffdZ/IEH4fDgZ49e3r3s7KycOzYsZD3bNiwAevXr/ful5SUoLy8HAUFBdF8GxErKiqKy3MpvlhvyYn1pjmg2+5x5pk4mpmJJgB2BWgBkJ2bi67FxSHvK3afr0lLg9Pvuq4FhcgoLkaV54CUSOtzNpzugbU9unaBrXOXqMrMuktOyVBvbU7mNnr0aIwe7ZsCd8stt+D222+Hw+FAdnZ20PtsNhvsuulx6enpaG5uDvla48ePx9ixY737niiwtrYWLTEclS6EQFFREWpqajg9L4mw3pIT6y24Q0eOQnW3Hrc0auvwOBob0VRdHfK+avf5lmb/EAU4VlcHceiw4ZgnQAGAQ5WVEI7GiMrHuktOVqg3u90eUQNDzDPO5uTkQEqJEydOhAxScnNzUVdX591vaGgwBC1m0tLSkJaWZnouHj9oyRwCSYn1lpxYb4Gk3Q7pP3BWKGF/Tr7zJtcpNrOjvnudzVEneWPdJadkqLc250lZvXo13nvvPe/+7t27IYRAt24ma0ronH322di1a5d3f+/evejatWtbi0NElDpstsAFBiOYPRn2mUqIsQgtga0vRInS5iDlrLPOwtq1a7Fz507s2LEDq1atwpgxY5CRkQFAG3ti1hUzbNgwfP7559ixYwdcLhc2btyIIUOGtLU4REQpQyi6IOXEMc/Btj3Ubg/9DAYpZCFt7u65+OKLUVVVhcceewyZmZkYMWIEJk6c6D0/a9YsTJ48GSNGjDDcl5eXhxtvvBHz589HdnY2MjMzMX065+cTUQd33teB/37s2/cPKPR5VIKQqhqQmdbLZg89q6MxsvEoRO2hVUHKunXGefiTJk3CpEmTTK9dsmRJ0OdcfvnlGDJkCCorK1FaWhpyDAsRUUcgsnIgAYhrJrsP+AUUNpNkbv5cruDdQmHul5v/DtH/vPCvQdQOYj5wNlpFRUVJMQ2KiKg9SE/G2Ax3rhL/IMUs46y/FicQZJIBnKG7c2RNZfjnE7UTLjBIRGQl+hWQgcDunkgScKmu4OcKA3OsGB5fen745xO1EwYpRERW4mlJ8XTXhJqJE/QZ5tNKxY9ugcjIDHmr3PJm9K9HFCcMUoiIrMSTt8ITpLQmdbk0aUkp7gVxydjA4/4cpyFP1YW/jqgdMEghIrIST0uKu5tHtKq7xx3o6BJ1KT+eYlizR9wwM/j9J0IvUULUXhikEBFZifTr7gkISiIJUlSTg8b7lG9dAWXGL83vb+I0ZLIGBilERFaihhk4G4QyfbZvJ9JU512CZAaPIBcLUXtgkEJEZCX+Y1L8B84GaUgRQ0cC6Vqmb+/snnDBiud6AGLsj3zHmdCNLIJBChGRlfiNSQno7gk1JsUT2MjAMSmm0tJ9292LgNKva7exu4csgkEKEZGVuMekeNPaR7NWj+daT6ATtiVFF6QI4ctGazqmhaj9MUghIrKSgDEpUQyc9W9JCZXUDQDSfN09kNL3WuHuI2onDFKIiKwkIJmb/xTkEPd6g4xWtKS0OH33RzrwlijOGKQQEVlJ2GRuIaIUb5DhDlL03TaZWYHX23Xr+7S0BLbEECUYgxQiIivxHzgbbDVjM55kbf4DZ88+FyjpH3C50AdALc7AMS1ECcYghYjISvyTudn9VjMONbvHv7vH/VWZNN0YkJhpcfoG60oGKWQNDFKIiKzEf+Bsmn+QEuJe/yDDP+AJRT8mhS0pZBEMUoiIrMR/TIp/S0okY1L8WlIimsbMMSlkQQxSiIisxD+w0CdcCydYMrdIWlIyszgmhSyHQQoRkZWEa0kJOSbFL8jw7zoyu+WWu4HzL4S49KrA2UFECWZPdAGIiEjHk0jNEzBE093jPyal4bTxuNktF34buPDb2m3s7iGLYUsKEZGV+Cdz8x84G4puTIr85IPA4+Eo7O4ha2GQQkRkJZ5WDG+eFJvxvP+qyIZzvpYQ+dF7uuM28+v9CU5BJmthkEJEZCXh0uJ3LQh+ry7IkKfqdMcjbUnhFGSyFgYpRERWEiYtvhh5afB79UGGPtCINGut4JgUshYGKUREVuI/cFbfVfONb0LYQnTd6IMMfWwTqotIj2NSyGIYpBARWYlfS4rQtYKIcEnZggUZItIxKZyCTNbCIIWIyEr8k7npu3tCtaIA3iBFfvoh8N+PA46HxZYUshgGKUREVuI/JkUfYIQLNtwBjdzypunxsLwtKRyTQtbAIIWIyEr8s8QagpQwLSm1NebHo2xJkZ99FNn1RHHGIIWIKAGk4zTkrp2Q/q0W0j1w1ju7J4qWlJPHg71aZIVqaNC+Vh+AbHFGdg9RHEWdFr++vh6zZ8/GAw88gMLCQgDA1q1bsXr1ahw5cgR9+/bFzJkz0bNnz7DPWrBgAbZt2+bdHzx4MObMmRNtkYiIko664BdA9QGIm++CGHmJFhR89QXgdAcHwqy7J8IBsP4CUusHcbret+1SuXAKJVxU/wXr6upQXl6O2tpa77GamhosXboUU6dORWlpKVauXIlnnnkGc+fODfu8PXv24PHHH0e3bt0AALZwg8KIiFJF9QEAgPzgHWDkJZB/WgX59l9951szJsVMUU+ICIMU6WrR7XDwLCVeVP/jn3zySYwaNcpwrKqqChMnTsTIkSORn5+PsrIyVFRUhH3W0aNHIaVE7969kZOTg5ycHGRmZkZXeiKiZOcOBgwBCmCezK01f8jl5EZ8qSGY4QwfsoCoWlKmTZuGHj164LnnnvMeGzp0qOGagwcPoqioKOyzdu/eDVVVMX36dJw+fRpDhw7FlClTkJsb+RuKiChlmQ2cDZcnxUxTU+TXZuj+UGRLCllAVEFKjx49Qp5vaWnBxo0bceWVV4Z9VnV1Nfr27YsbbrgBQggsW7YML774IqZOnRr0HqfTCafTN5hLCIGsrCzvdqx4nhXLZ1L8sd6SU0evNyGl6fcu0tK14/pxKDZb1D8nZcLkiO8RZ53jnb4crFyG6zt43SWrZKq3mA6LWrt2LTIzM3HZZZeFvXbcuHEYN26cd/+6667DwoULQwYpGzZswPr16737JSUlKC8vR0FBiAW32iCSFiGyHtZbcupo9XbA/TU9zY7C4mLvvkdxr14Q6RloOlGLw+5juXl5yC8uDvtMvaIRF8HWOT+iMskf3ojKPywBAPQoKICtS7eI7utodZcqkqHeYhakbN++HZs2bcL8+fNht0f/2OzsbNTX18PpdCItzXyQ1/jx4zF27FjvvicKrK2tRUtLi+k9rSGEQFFREWpqagKnB5Jlsd6SU0evt6amJlRXVwccr649AqEokMd804pPNTSgweTaUA4dqYVwNER+g6IAqopDNdUQjc0hL+3odZesrFBvdrs9ogaGmAQphw4dwqJFizBlypSIph4DwMKFC3HVVVehf//+AICKigrk5+cHDVAAIC0tLej5ePygpZR84yUh1lty6kj1pv7z77odk+/bbgeE0H4m+sUB3ceiIYUSXQZZoQBQIV1qxPd1pLpLJclQb21O5tbc3IwFCxZg+PDhGD58OBobG9HY2Oj9xh0Oh2krR58+fbB69Wrs2rULH330EV566SWUlZW1tThERJYn1zyt2zEZoKqfZaMfN9CaKcjRzgjyvAYHzpIFtLkl5ZNPPkFVVRWqqqrw1ltveY8vXrwYhYWFmDVrFiZPnowRI0YY7hs3bhxqa2sxb9485OXloaysDOPHj29rcYiIkkvdicBj+iClrcncWhukcAoyWUCrgpR169Z5t0eMGGHY97dkyRLzF7bbMWPGDMyYMaM1RSAiSg1HDgUeM7Sk6IKM1rSkRDtt2bvIIIMUSjyu3UNElEhSQqou47H0dN+2fkxKK1pSop5mypYUshAGKUREidbYaNzPzPZtR7PAYCyECFKk4xTUFQshd3CVZGofDFKIiNqJPFUH9fcLA080+QUpWbogRR+YtMf6ZiJEkPLKC5AfvgP1yYfiXw4iMEghImo3csMftAUF/TX65THJzPJt67trWpMWP1re2T3Gqany8+2Q+qnTRO2AC3ETEbUTeazW/ESzsSVFGFpSjGnxIyUuHAN8bUTY6wJvdAdFfi0ppi1ARHHGIIWIqL3Ygnzk+i8CmBmkuyeKMSnKLT+LomD6G4N097iMg3tl3QnjgoREccDuHiKi9hJsyZAWv/TzWUG6e8LM7hFXT9K+Xje9NaVzPyRIMjf/lpV7boTrZze0/nWIIsCWFCKidiJsaTBNQt7sF6S0siVFGftjyG9dAZGX39oiBm9J8Z8mDQDNTYHHiGKILSlERO0lyJgS6XQaD2ToWlKinHbcpgAFAGprtDIdP2o8HiRvitXXfqHkxiCFiKi9BOvucfq1SOiTueln9ESbmK0tPv3AuG/WkgIALU7z40QxwCCFiKi96IOUgUN8LSv+LSnB1u5pjyClcxfta7dC43FXkJaUpia2plDcMEghImovNl/wIc6/QBek+LWkBAtSEP8gRQy5ILAMQNC1fKqnjIPr17dBOptNzxO1BYMUIqL2oh+TIqVvto5fS4pI0y8wKMw24yfKtXvU+pNA9QHg88/iWCjqqBikEBG1F32Qkp7hy5vin+QtaEtKO2jtAoOultiXhTo8BilERO1F/4s/PcMbEMjNrxmv04/xaO+Bs960+EEGygahLpkP9bX1cSgQdWQMUoiI2otuhowYMDh4Blp9mvx2HpNi1pKivv3XiG6VLz/PQbQUUwxSiIjaizu1vPjmtyHyu5rnTek3EBg8zLsrDAsMxruAMF0FWb64PPL7TxyLcYGoI2OQEoR0OiGZTZGIYsnzi79rgfbVZLyJ7d5yiPSMIA9ov5YUuelVuJY8HPXt8ssdsS4RdWBMi2/CtfB+VP7vUwCAsuxliGAJmIiIouHp7vHM6vFvSelVEvr+9hiToh8D88n70f+x1tL2AbSyah+Q3w0iJ7fNz6LkxpYUE1I3lU5ueZN9rEQUG56WlCDp8ZUp97RjYYLwb91pbgJyO0V+v6ttGWjlgT1QH7wD6pwZbXoOpQYGKWZ0SYvkmqchP9icuLIQUepw+bWkuPxm0PgnUPPXnnlSPJoaIQYNjfx+/+8pSnL7Vm2j/mSbnkOpgUFKBORf1yW6CESUCjwtKcFykQRpYfFpx9k9Hk2NxinRZrp2B/oO0Laj6O6Rdcfh+vVtUN/YoDvIlmvyYZBiJuBN2pCYchBRavGMSbG5P2P8E6ApYYKUPv1iXyZ//p9/jQ1hE7uJoRdBFPXUdqIJUt54Bag+ALl+le/Yq3/0bTPVfofHIMWE8v1JxgPhmmCJiMKQx2ohPV0Ywbp7bOYfycpvX4Ay/2mILt3iWELPixnLIA/sgQzXhaMovlagqDLPhmk1OX0qimdRKmKQYkIZ+yN0vkE3aCstPfjFRERhyPqTUO+9BXDPGvQGKf4tFEFaUkSnPIjCM+JYQv2L+QUpLyw1JKEzvSWvs2+F52iClKBTrd0cDFI6OgYpkeiUl+gSEFEyq9xr3Fda2d3THhpNureDtaTkdkLWN78N8e0rfSs8RzMFWRekSFUNnEnJIKXDY5ASjL7J88v/Jq4cRJR6PF0j/i0UYQfOxp/8+P3Ag0GCFDF8NLrf/5iWfK6tLSlNjUCzcQyKPH408mdRSmKQEkTu9yYY9mUTs88SUSv5J2HzjkmJrLunXZklb3MHU+KmO4FvjPQd17d8pLlbUqL5rNR/v47TUOf/zHh+9/8ifxalJAYpQSi5naD8bK7vgH7BLyKiaAQEKZ4pyJENnG1XZt3bn28HAIjMTCg33+k7rh+/kp2jfW04Hflr6Vpd1KcXANUHDKflga8ifxalJAu8I6xLDBzi22nmVDgiaiX/7hJPC4K+JSIrJ2DQaiIoP54a/KRQIDKzdBfrypulBSny8+2Q27dCtkSQeVb/c9m7K/D8rp1QN/89/HMoZSX+HWFhQgjvGw9B5uvLPbsgqyshjx6G3GPyJiMichp/YQtPi0nvvt5jyuPPGVc8TpTC4uDnPDMdO3UGAIjzL/CeEhmZ2kb9SahPzYV86ffhXyuC8Svyj0+Hfw6lrKhXzquvr8fs2bPxwAMPoLCwEACwf/9+LFu2DDU1Nbjkkktw/fXXR/Rm27lzJ1asWIG6ujqMHz8eY8eOjf47iLf0dK350iRIkfV1UB82rrWhlK+E6Nq9vUpHRMnAv1XB3QIhLrsa8qXfQ7npjhArH7czW4i8UO5xJ8pDS4BDVVDOKfWd6z/IcKnc/BpwXZj1d0LlX7GnBf7cqMOJqiWlrq4OCxYsQG1trfeY0+lEeXk5SkpK8Mgjj6CyshKbN2+O6Fnl5eUYNWoU5s2bhy1btmDHDgsu8e1J5GY2mOxUXeCxwwfjWx4iSjr+XR/y2BEAgPLNb0P53QsQ51+YiGKZSwvxt6v781B0yoPoN9BwSuTlR/9ajuDjV5QHn/Juux77JRd67aCiClKefPJJjBo1ynDs448/hsPhwOTJk1FUVISJEyfi7bffDvusLVu2oEuXLrjmmmtQXFyMCRMmRHRfu/P8dWMW0Zs1FjU64locIkpCfp8foouvtdUSXTw6ItQMoxgntpT/eDX4SU/3EaClgfDPNUMdQlRByrRp0/C9733PcGzfvn3o378/MjK0X+Z9+vRBZWVl2Gft27cPgwYN8r5B+/Xrhz179kRTnPbheVOaDZw1SVokmcaZiPz5dxcPGZGYcrRVWuglQsTV10X8KOkM05WTlW3cP1QV8bMpdUQ1JqVHjx4BxxoaGlBQUODdF0JAURScOnUKubm5QZ/lcDjQs2dP735WVhaOHTsW8vWdTiecuv/YQghkZWV5t2PF8ywhhC9IaXEGvoZJf6o4fcpyfxl1FIZ6o6TREepNtLR4V6lRJt8BxQJJ21pD2GyGevKvO2XkJXDpFgjUnwtwut78+Jl9oPx4KpTMLMjvXA25yd3a4jid0v9H2lMyveeiHjjrT1EUpPlF1+np6WgOM2XXZrPBbve9fCT3bNiwAevXr/ful5SUoLy83BAkxVJRUREO53ZCE4D8nGzkFBtHvTedqMVhv3tyFaBzcYjR8RR3RUVFiS4CtUIq11tdViZOAsi+dCy6XTs50cUJ60CQ4wXduiPN5PPNW3fFxaibfBtOrl6i7RYUQARpfWmo+gpH3NtpffvD+dWX2rMefAJpZ/TSTtw1B0ebGuD415vIy8xAJ362xlQyvOfaHKTk5ubiwAHjf+mGhgZDABLsvro638DTSO7xnwHkiQJra2vREs16EWEIIVBUVISamhq0SC0j5InDh1BXXQ3AvXx4czPknoqAe+trquFwX0ftS19vHGSXPDpCvanHtVbihhYXqpP186HHmai1Z0Doym9Wd/LCSwB3kFK9fx+QmQX19wuBwmLYxl3vvbflAV9SONfg4YA7SKltUQ2v4VK159YdPYJTyfqzsxgrvOfsdntEDQxtDlL69etnGPB6+PBhOJ3OkF09AHD22Wfj3//+t3d/79696Nq1a8h70tLSAlptPOLxg5ZSQrq7e6SzWdv/36dQfzsn+D31J1P2gzZZSClZB0koletNelqJ7fak/R6VO7TPPbPy6+tO2uxahl0pIZ1NQG015If/AgCoV0yAyMjwzm7y3q9fSNCeZnwN92ew+vLzkFJC+d4PY/ltdWjJ8J5rczK3gQMHwuFw4J133gEAvPLKKxg8eDAUdx4Ah8Nh2soxbNgwfP7559ixYwdcLhc2btyIIUOGBFyXaCLNPbvn+BHI/RUhAxQAwMnQ42qIqAPyzO6J8eyYdhVm0KyHEEKXuqEZ+mmQ8p3XtA3/dPehMu2m+35mcsMfIioDpY42Byk2mw3Tpk3DihUrMHXqVHzwwQe47jrfCO9Zs2Zh27ZtAffl5eXhxhtvxPz58zFt2jQcOHAAP/jBD9panNhzvzHla3+GOvfu8NfXnYhveYgo+XjyLEX4i96Sogmw3LOZ5HtvG2c2ufNIyfqT3kNi3PUQZeOAPv0gJk03eV1jkjsZIs2DbG6Ca/E8qFvejLysZGmt6u5Zt26dYX/EiBFYtGgRKioqMGDAAOTl+RaoWrJkSdDnXH755RgyZAgqKytRWlqK7OzsoNcmTLRZIJu4ECER+UinE/Kd17UdzzIbFqfMmA112QLjwVa0AsmNL0L0P8+3/4U7Yac+SPn6hRCdu8B2/29NnyF69oGhQ6LuJJCZDVm1D8jLh3Cn6AcAueEF4NMPIT/9ELJfKeSWNyCu+AFEXpeoy07W0OYxKR5du3YNO6bETFFRkbVHGNuj/MuHLSlEpCPf0S2Q55/7w6LEN0ZCWfZnqL++Dait0Q5GE6TY7d48UlLfqlFTCfnlDl+QMnQkxBm9Qz/rbGNmWzQ3Qh46CPXBO4CsbNgWrdVe5+RxQ3I4tfxe4HQ95OFq2G6/P/Kyk6VwgcFw0iN7Y4oJN3u35Sfvx6s0RGQRctdOyC8+C3/d9v94t0WStKQAgLCn+QIUaDlSIqXMesS77Rk066Eu+o0394koGRD+YZ27ALqFDNWX/wD1fne3UIMDssUJqbogP99uvM+Th2XXzojLTdbDICWctPDdPWLKPRCX+KZGy88Cx+AQUeqQzmaoj86G+vivIOuOh75Y3xrrTj6ZNJRW/orI7xb8nL5LPCf0LFBAG4hru+1XvgOf/cd4wZFDUJ+aB/n7heYPcDALeDJjkBJOU0PYS0TvvlrCom7aqtBQrJ/Fj4jaQDemQr1nMtS3Nga/Vv+LPolaUgAAqtq6+yJcbFDEYnmAQ9XAjo/a/hyyJAYp4Rw5ZHpYeXAxkNMJGDIColjLjiiuuAYAII8fba/SEVEi6IIUAJBrV8BVPhvSLKmkfnptembg+RQk7Paw6xOJoaMMg15bS37+afhrWhtsUcIxSAlDXHoVYLNBXHY1lMdW+U70KIby2+ehzPil79ou7ibOE8yVQpTSGk1aWHfvBHb91+Ri3dyUMFm1U4lyw22hL+jTL7rnBRn8Kv/xl/A3N3PWZbJikBKGKOkP5Yk/Qlz7E4j8bhDXz4S4+S4IexqEYjMOJvP0w55gSwpRSnMFWYZDmvzFfnC/b7vAwjMZY0x07gIMDJGgUw1coDXk84aMgJj6c6BLdyizH4UY813jBQOHwLbiL8CAwQH3ys2vRfVaZB0MUiIgMrN9q3xefAWUkZeYX+iZXtjIqJ0opbmCdB+4u3bkJ+/7EoqdPAEAUOYug2jtQNQEEZddrX31DwgipFx7i/HA+Rf6tk1WkQ/7vBHfgu3RlRBnnwtxxQTjyUxtULJy670B98k/r4b6/uaoX48SL7neMVbnaVWR0b/5iCiJBGsFcLkgVRXqkochn18MWVPlG3yfnWSDZgGICTdB+eVjED+a2rr7e54F4Q5UxLRZsN12n+9klC0pAc/uVgBluS8vijLme9rxTnmm18tnfwvpPzOILK/jdJC2B8UdpAT7K4uIUkOwVgBnM3DKt7q71K9RkySJ3PSEzQb0jSCXSQjKd66GHHkphGe68aChwI6PIEZd1vbyCQHlyTXAoYMQJf0DLyjuBVQf8O6qqxfD9vhzET9ffvUFkJ4O0bOkzWWl1mGQEkueptw2/oVARBYX5D0umxohTvrypnjT4WdmQSTz4oJtJHT5UJQ75gCNDojs8DlSInp2di5gFqAAQH5XQ5ASzcBlWV8H9ZFZAABl+aveLn9qX+zuiSVPS4qUnPJGlMJksJaUU3XGldD3V2hfo5zJksqEosQsQAnK3fIhLhxjzBp+qg5S+mZbycq9UJ/9HaQus66Xvh4bTsepoBQOW1JiyaaL+VS19dkaicjaggUpdcchHbpfaA3uFXtzOsW/TOSl/OIRoGovcPZAiN59Id98BfL//qllu21uAjK0fDXqwl8Bp+ohD3wF24NPGR/idHo35T/+ApQMgBg8tB2/CwLYkhJb+qCEXT5EqSvY+/tUPeTGFwMOi4yOkcTNKkRWNkS/UgghIHqWQNx8l29igz6IPOVe36dqX+BDGnzp9OXGtVAXPRS8BY3ihkFKLCm6nCkMUohSipQS8tgRrbsg2Pvb03LiL5NBSiIJIXxLEkS4lo/0BDB6tdUxLBVFgkFKLBmCFI5JIUol8vU/Q733J5Cvv+ybwec360MGG7uQbGv2pCLPOJjT5kFKwDjCY7WBF+kT81G7YJASS/ruHk5DJkop8uXn3V9X+1pS0tKMF/33Y9N7xdnnxrNoFAnPDCN3S4r0CzjkmqchD+taSvzWZwIAeexI3IpH5hikxJBQFMAzTY3dPUSpyzM2IdJpxUVnxq8sFBl3Mj3pDlLU5Y8ZTst3Xof6qG8tNjSZZA43W7OJ4opBSqx5unzY3UOUujx/hNjTzM/3MnYDicIz4lwgCsc77dkzcNasO0c/7bi5KfA8g5R2xyAl1mxM6EaU8lxBuns8OuVDeXg5xDWToTy1tv3KRcF58qU4m7WvQRaJdN06DvJoLWSTSZDSxCClvTFIiTXhSY3PIIUoVclPPwQAYxbZwmLfdtU+iIIiKFdcA5GZfOnwU1JahvbV2Qx59DDQ3Ow7163Qt62qUFc8Zt6SEmz2FsUNg5RY87aksLuHKGXt+VL7qm9J6dzFt63vNiBr8NRVczPU2VO8h8XYH0F5ZIXx2orPgR0fadsDBkOM+BYAQLK7p90xSIk17yKD5k2J4ci9uyBPm8zPJ6LEMlu7JT3Dd7qop2/7hze3R4koGu6WFPnpB75jmVlQrr5OS/p2/czAe7JyoMy8D/jacG2fQUq7Y1r8WPM0/7Y4Q19nQn1zA+SfVgFn9Ib49pVAxf8gJv8UIopFsYgoThRb4B8fmVkQU+4BFAWisBjyvbeBwcMgLrs6MWWk4Fzuz+SaKu8h5bHnfNsXXwE58hKoMyd4j4khwyGyc4DMbEiAQUoC8LdfrHmaFJ3Noa8zIf+0Sts4uB/yj8sAAGL4aF8UT0SJY1MA/6Fmig3KBRf7dhev4x8VFiX/t92wLy69CiIzy3gsLR3KLx/zrn7sbRnPcl/HhQbbHbt7Ys09sEpWRZeZUL8yp+F4kOyIRNTO9BmlPWzGYwxQrEu59hbfTl4+lB9PNb1O9B3g3Zae2Tzdemhfjx6GbGldVz61DoOUWHNnKZQvLA1MsxxK5V7z4yc4AI/IEsyCFLNjZEliwCCgTz8AgDLjl6Evds/2EYPcqx537Q5kZGmzNiNYv0eqLrge/xXUlU+0pcgEdvfEV+UeoPfZEV2qPl1ufoKzBIiswSytgMK/85KJ8vP5wNFaiDN7h77u109A7tgG8fVvAnAvUNijGNj/FeR/t0EU9wr9Qnt2AV98po1j+cldMSl7R8V3WBxJ3QCtsA4fNH/GiaMxKg0RtYnJ5B62dCYXkZkVNkABtOy0yohvQeinmHfqDACQb2wI/0K6gDZYVz5FhkFKrHXu6tv+ckfk93nSZvcbaDz+0XsMVIisoEfg+juy4n8JKAglgjjrHG3Db7CtOV1gIpkzqy0YpMSY8quF3v5M+c7rkec8cWc3VK65KeCUuvQRw76UEur656Bu/nubykpEUWhl7iNKDWLAYG0jknFI+sYTF4OUtojZmJTNmzdj6dKlAcdnzpyJMWPGBL1vwYIF2LZtm3d/8ODBmDNnTqyK1e5El24Ql4+HXPOMduDIISCnU/gbm90rbppdu+dLyBYnhGcxs5pKyDdeBgDI4aMhInk+EbUNl7ro2DwtKGarI+tIKY1rt6kuAEHWeKKwYhakXHTRRRg+3JfPo7GxEb/4xS8wcODAEHcBe/bsweOPP45u3boBAGy25B8tLy4Y4wtSgnw/8uhhLSCp+Bzq2hW+lTkzsyBGXAxZcwDY/5XvhoMHgN59tW39mhInjkUWBBFR25gEKeLbVyagIJQQGZna1yCLDEpnsyERnBeD2zaJWZBit9th1+UIeOONN3DBBRegR48eQe85evQopJTo3Tv8QKZkIrJzgPxuwImjpv9BZW0N1PumAUVnGrIfAgAyMqBMvQcA4Jr6fd/xE0e9QYq6+infcWcz1L+tg9y+FcrdD3ExM6J4MVnZXFz0nQQUhBLC05Lil3VW7v8KOFZr+v8DQPDjFJG4TEFubm7Ga6+9hvnz54e8bvfu3VBVFdOnT8fp06cxdOhQTJkyBbm5uabXO51OOJ2+dPNCCGS5MwEKs3U1WsnzrDY90661oAiXK+A58nN35kOT2T8iO9d7vTL1HqgrFmon6k/6nnNgj++Gk8chX3lB2/7gXxBjvtv6Mie5mNQbtbukqTf/Pzg6dYbSwacgJ03dxYLnD8CWFsDVAmFPg6w+AHXuXSFvE6pquZ9PMtVbXIKUd999F+eccw4KCwtDXlddXY2+ffvihhtugBACy5Ytw4svvoipU80zAW7YsAHr16/37peUlKC8vBwFBQUxLb9HUVFRq++tTs9EC4Bu+fnIKC42nHOccSbM5uvkfOcqdD3jDN+BcRNR84+NcO75Ep2kC3nu5xzQ3aMunufdzuuUi05+r9URtaXeKHGsXm9VUkI/BNKWno5ivt8AWL/uYkE6nah0bxfl50Pk5KJyylVh7yvs3g327sF7FBIpGeotLkHKpk2bcO2114a9bty4cRg3bpx3/7rrrsPChQuDBinjx4/H2LFjvfueKLC2thYtMUxVLIRAUVERampqWj3HvcU9vPvI4UPA669C3fgibDffBXFmH6gHzfOnONIy0VRtzGboGjAY2PMlTr7xKk5981Lf4FkTdUeO4FR1+GyIqSoW9UbtL1nqTW0xrsflOlqL6g78fgOSp+5ixm4HWlpQs28P5EfvmV6izLwP6tKHvfuHq6shnNaa4WOFerPb7RE1MMQ8SKmpqUFNTQ0GDx4c9b3Z2dmor6+H0+lEWlrgL+O0tDTT40B8EuZIKVv/3ONakie5v8K7cKDrt3Nge3w1ZLAssnn5ga+Xrw0oRk0l1FfXQPnBjcHLW3eiY3xQhNGmeqOEsXy9mY0vs3J525Hl6y5W0jOBllNw3XuL4bAYOgqywQHllruAZmMwK10tgEV/NslQbzHvUH3vvfcwdOhQwyDaYBYuXIgvv/zSu19RUYH8/PyggUhSca+W6V3ZGNDGjzQ2QO7dbXqL8AQk+mMDzvNuy9fWB5zXk5tfa0VBiSgcuXeXd/FQ6sAc5gu+KtPvhe3uhyDyugD+rd3Mk9ImMQ9SPv30U5x33nmGYw6Hw7Q7pk+fPli9ejV27dqFjz76CC+99BLKyspiXSRrqa40DnzVKzDpH+xuPCb3VwR/dnp6GwpGRMGo8+9JdBHICoaODDik3Pe48YD/H9lMAtgmMQ1SmpubsWvXLvTv399wfNasWYaEbR7jxo1Dz549MW/ePDz33HMoKyvD+PHjY1kky5H7dmvT1fyIYRcBvUoCj2dmAWf4pmirc+/2nZv6c+PFXEKciChuzDKCo1df435AS4oL8sQxqP94FTJISwwFF9MxKenp6VizZk3A8SVLlpi/uN2OGTNmYMaMGbEshqXJPy4zPa7c+oug9yhT7oH6mzsDj4/4FuR53wDqjkP99W2As9nkbiKKJeXWX0Bd/RQU/z8SKOWJgiKI66ZD/vFpbX90GYT/0Ab/IKW5CeqyR4CvvoD8z7+h3PUg81lFoWNP8k8WnfMDDolhF2lfc3J9GWdbnJYfBEWU7MSwi6A8+SLE14aHv5hST1aOb7tPv4DTwmYD8vK9+/JfrwNffaHtVHwO9de3Q7Y4A+4jcwxS4kRMmxXxtcpDi0M/K68LxPf8pnR37e7bTtONRWGXD1HciQ6exK0jE9m+IEV0yjO9Rpm7DCjqCQCQ7282njx+BNgXYmwhGfCdFieh8pkEKApcAj7geZd933igp278iv61TtVF/rpEFBZbJ8kgS9dVo29V0RHZOUDXEDlAThyFdDqhvvQs5P8+jXEBUwuDlHgZEDpPjBg+2rcdydLfOcalAoR+Jo+uT1T+41XDdeqWN6G+/dfwzycic6fqvZtiCmf5dHhZus/ibPMlXLTrsoKekvUnIV9bD/mPV6H+dk4MC5d6GKTEicjOgfLMBvOTRWdCXDUR6HkWxA9/EtnzFMUwRVmqvr/u9OsvyK3vQn72H227xQn5/GLIF5dDnjBLxB+abG6CrD8Jdf0qbdVmoo7IMxsvLx/KBRcntiyUeMVnAiX9gfyuQI8zgl4mep5l2FceWATv2mpHDkPu+m/8yphC4pIWnzT+LSTKb5ZA7vkSot9AiMIzYHtgUXTPu/JayOfc90i/BEHZuVqioeNHoC76DZS5S4FOnX3nT5/yZa+NgDxyCOqDP/UuSy7f2ABlyZ8g0jOiKjNR0vME+F26h76OOgSh2KD88jHA5Qqc2aO/7pxB8P4pabMBZ/bxTleW+ysAz0KzAKSqcpxTEPyptJfzL4Ao7gVl5KUQhcGj71CUUZcBpV8H8vIhBg8znBN+SYbUOTOh/vbX3n35348DnqeuXwV19VMBfe5SVaH+cqo3QPEe3/Jmq8pNlNQ8U/szGKCTRggRMkABAHTp6tu22bV7zuil7fuPQzl0MLYFTCEMUtqJWcr71lDuehBK+bMQWX7z7P0TCgGALjutf4Ahqw9AvrEB8t1NQI3fgodf7jB/8eNHWlNkoqQmPTPmbGx4pihk6sakNDdpX90zfgLUn4x/eZIUg5Q4U+6YAzHsIohx18XkeVoEHzhzSFx8RWASIb0zekFd/hjUN93jZBynvafk1i1Q390E1+wpkDs/hrrwfvNnODm3nzogz8KCDFIoGiYJ20Su+ZRlMG9KUHzXxZn42vB2SfokFAXipp9C/n6h+QXb/k/rH926BbLvuYb1JOTGF7XxKieOQv3dA8FfxK/7h6hDcLl/gUSwaCqRh0jPAArPAA4fhCgb5zvRpx+wz73IbNcCbWA2g5Sg+K5LIWLEt7SVlv+0MuR18qsvIM7sYzwYavZPrxLgwB7I3Z/HoJREScbbkhJBqgAiHdv8pyGbmoyLv+qD3a7dGaSEwSAlhQghgO9cDUgVcv1z2kG7PTALbU1lyKlzHsqjq4C640BWDtRf3QocPaxNa/77em2GUun5Mf8eiCzH3eoobFEkaCRyE/4Drht1LdLuLnrpdEKAzDBISTFCCIjLfwDZ91zIz7ZCnDMI6qKHjBc1NoSP3M+/AKJLN6BLN8hGh3asxQn5wb8gN74ICUBZ8HuIboVx+T6ILMM7cJYtKRQDnkG0gG8coQWXM5FV+6A+8yjEVROhDL8oYeXgwNkUJc4phfKDyYDJQC3Z1AhpMghWjLvetz10lO9ERhYgtP8q8rknfccr98asvESW5enu4ZgUigWzIOVITWLK4ke2tEB9bT3kni+hPvtboPoA5PJHE1omBimpLttkbYntWwNbUkrPN6wPJL5+oW9biMDkcQDkgT0xKyaRZXEKMsVSU6N3U6S5u3v++hKk37prUlUhPQGyCXmqDnLb//mmyMeA/M8WyJefh/rwzwGLfL7zXZfihH7syZl9gKp9AAC5+invMXHJlRDDRkNkZMK24i8RP1tyJU/qCFzs7qEYavK1pEjdLEvs2gm4/ziULU6oM64BCouhzF1qur6b+tRc4KsvIK76McT3J8WmbCdPxOY5McSWlA5AKV8JcfNdUMwWR6s7AeVbVxiWHw+rtztxnONUbApIZGWertG09NDXEUVA3PRT7esPboT4hi9TuDxxzLutrnhc2zhcbVjg0uCrL7T7/vVG7AoXze+BdsIgpQMQXbtDGXmJ1pLif+6CMZE9Y9osoN9AKAuehXLNTdrBL3dAqoHdQEQpxTOGgEEKxYAy8hIov3sByncnGJczcS/iKh2ngW3/5zteWwOpH8fi73SQIKY1dF1RXgler41BSgcihAByO/n2x10PcXVkzYTK8NGw3VsO0a3AsBozjhyKdTGJrMXp/gXBtXsoRjyZZ4U9DeKHP9EOelbbrj5guFZd8Auoyx4J/rBYzgwyC1Kam2I67iVaHJPSwSg/fxjyn3+DGPujVq8nJPRBSt0JoLDYuysbHYCqQmTntrGkRBbR7F5gkC0pFA/u1bXlcXdCTbM10nZsg2xsgNCvBxQPZkEKoHXt5+XH97WDYEtKByPO7APl+pltX/CwXykA95LjbrKlBeqDP4V65yTIHdva9nwii/A2tSe42ZtSk3ex2KYGyP99CvWZIFN+j9bGvzAmLeNaOgoZ/9cOgkEKtYrofx4AQL64HOqWNyEPH9T+AnD3q6pPPggZLConshh106tQX/uz+UknW1IojjIzta8H9kD97Zzg1x31CyB0rdWxGBsoVRVy6xbDMTFtFpTp90LkdWnz81uLQQq1irjw295t+fxiqL+aDvXF5YZr1NuvhTzWDtE/USvJ+pOQOz6CXPcs5MurIWuqAi9yt6QItqRQPKRnmh/3y+Ytjx6GPF0P+fl2SCmBHF2X+n9j0HLtn1KiS3eIYYnLNOvBMSnUKqK4Z+DBz/4TcEiuf06bGUSUYFJKbfC4jvqbuwyLa8qvPocoOtN4o2dMCoMUiodgmYy79/C2TAPQgpM1zwAAxJR7vFnAAUDWnWzz2j9y+1Zto0t3iMu+D/GtsoD3SyKwJYXiyr/5kCgRpKpCfWQWXL+do/0V6uG/+nd1ZeDN7O6heDIZkCou+g6Un9xtPKiblizfeBk4fNB3zhliinKkaqu11770Kihl4yAys9v+zBhgkEKtJn40JfDYqEuh/GYJ0H+Q95isO96exSIKVH8S2PMl8L9PgUMHIZuboLr/KjXwyzkh6+uAQ+4uIE5BpjgQuXkQk+/QHRBQJt+h5bdauFprNfHnl7Jerl/t25YS6r//AVm1H7LuBOTeXRGVwzuGMCvOM4iixO4eajXlsu9DXnKl1mTuTrePkgEQxb2g3P2QltYZ0FItJ3DgFRH06cePHoZ86y+Qm18LuMyzfoo8VQeRmwf5D90yEWxJoTgRg4d5588ov1roO57XBejdN/zcmqYG76b8z78hn1uk3ZOVDTQ4oNy3EKLknDDPcAcpGdYKUtiSQm0iFBuUH0/17Y+6VPtqTwN6uPv2G04b7pENDm1GUFMMmiiJgpC7dmrdO1X7DQmv1LXLTQMUAMDpeqhbt0C9+3qob20EGnRLP3hWrCWKNX1eKb9gWBT3ghj7Y/P7zNaT2vOFb7vBAQCQn20NX4ZGLdARGUEG8iYIgxRqM3Hu16AsexnK8le14MTDPf9fvvc2pOpbzVNdvQjy+cWQzz3Z3kWlDkR9dDbwv0+hrvydcdVvsxk8Hl/+F3L5YwAAuXYF0Lmr71z3HnEqKXV0Ii0N4qLvAOdfABT3CjivXD0JthV/0RaA7X227z7PEiWA7zPWZLCr3LgWMkT6fHm0VusOBQAGKZSKhN0eMBJcuD/U5b//AfWnE30DFj96Tzv+n3chT3K8CsVZ3fGwqcOV+39relzu/woAIC4ZC6Hw45LiR5l8B2y3/SrsjBrl7oeAgUMgbroTYvR3fCfC/B+Xa1cEP/fn53w7+V2DXpcIfNdR3IjvTvDtNDUCRw5B+mc0dI8oJ4qbE8eMLSn+0tKBosC/XgEA295zX8OuHrIGkZsH28/mQhl1KWDT/b/0BCmKSRcQAPn+5qDP9IzFAgB0LYhBKWMnZgNnV65ciddff92736NHDzz11FMh79m5cydWrFiBuro6jB8/HmPHjo1VccgCRO++ENfNgPzjMu1A3Qmoy/1SPp8+HXgjURup/3jVuL/i8aDXKvfMg8jIAM4+F6j43PwiDpolK9KNSZHv/xP49pUBYwD1ZFNjwJgTWV2pzXqDlgLfamNSYhakfPXVV5g9ezYGDBgAAFDCNI3W1dWhvLwcV111FUaNGoUnnngCZ511FgYNGhTyPkouypjvwvXeW8CeL6Gufgo4Zlw8SzpOtTkJEZE/+dKzxgOhVuvu4l7HKlRqcZm4tUuIghGKAtjsgKsF8sXlQKfOkCeOBb9h/1fAOaWGQ/KT933PG3ddvIraajHp7nG5XDhw4ABKS0uRk5ODnJwcZIWZa71lyxZ06dIF11xzDYqLizFhwgS8/fbbsSgOWU1WjvZVvwS5Z3BYqEGMbcTZQxSUflaEe3q8GDgk+PUOtviRReky1spVTwKffhh4jbsLR331j5CNDqhvvoJji+ZB/fj/ID2ZwrsWQBSZZBJPsJi0pOzbtw9SSsyaNQvHjh1DaWkpbr31VnTv3j3kPYMGDfIOEurXrx/WrFkT8nWcTiecTl/fshDCGwzFMn2v51lWSAmcEhynAg4pF30H6p9WAocPmv6cpaoCQkRVB/p6U//6EtRX18D283kQAwa3vuwUd7F+v8kIWj2UibdCVu2DyO8KxT3eRBT39OajEGN/BPnhv4DD2pgpZbQ1UoRbDT8rLcCm+zXuyY7sRxnzXagvPw988RnUO7TpzKcB4I1XfI/56a8tWY8xCVKqqqrQq1cv3HzzzcjLy8PKlSuxfPly3HfffUHvcTgc6NnTF7VlZWXh2LEQzVQANmzYgPXr13v3S0pKUF5ejoKC+Az0KSoqistzO5pq1QX9uHMlLx9d+vbDUWgzfArELNh166Ucf/oxnNr4EkRWNs7445tQouwjLSoqwoFXXgAAqEsfRs91m6O6X0qJxg/+BfuZfZDW66yo7o2Huj//AY63/4aC+Uths9jI+1iK1ftNOpthktweEAJFy/4E54E9yPrmmIAP5IbiM+HpjOw2fCSyZsyCeqoeav1J2M3WqiIvflYmTlV6BlSTPwQL5i7G0d89iK53/ApwuXDk5dDPKT5/GIQFB4jHJEgZPXo0Ro8e7d2/5ZZbcPvtt8PhcCA72zz/v81mg13XTJWeno7mZvMo0MN/cK3nQ6a2thYtYaZfRUMIgaKiItTU1ET0VxmF1lJ3wrCvNjhwIs0XeFTfcjVsC58HcjoBJ4/BtfElAFrSt5r3t0D0j2yckr7ePOTpU6iujm4Gkdy3G665Wipq28LnITonNltuy0otn0z1Mwthu/H2hJYlHmL9fpO6rhlx+XiI3n217REX44gQQMm5OKn7P+K9L9PdLSkUHM/tghPe/zc2IMr/Qx0FPysTT+2UF7gGVWExjhf3gfLoKpwAIEPNbnOrOXIk7DWxZLfbI2pgiEta/JycHEgpceLEiaBBSm5uLurqfNOeGhoaDEGLmbS0NKQFifTi8QaRUvKNFwPigjHaglgeGRmQvc/WZky4myfVLz6D/MuagDEqrkd/CeX2+yGGjIj49fzrTP1iB0T/8yK79+P3IT3TTgGo726C8r0fRvza8SSrD6T0/8dYvd9ks3sskhAQ19xkaDEJ+fweZ0K580FtvEpefkr/rGONn5UJVFgcsJYP6k4Y68Nmh7jlZ5DPavmAlB/+BMU//gmqrrkIACDKxlu2/mIycHb16tV47z3fB/vu3bshhEC3bt2C3nP22Wdj1y7fwkd79+5F166p25TdkYmx10LcfCeU2+8HuhZAmf5LCCEgrtaNJN+xLeggWvXpBW16ffVvLwUck40NcM2ZAdfU70O6p53KE0ehLn3YkE9AbvhDm147pnbtjOgvog7Pu2pxWtR97GLQN0IPoCWyGFEywLfj/r9rlkZffOObWhbw7j0gho2CkpkJ25I/Qbn/d1B+eHN7FTdqMWlJOeuss7B27Vrk5+dDVVWsWrUKY8aMQUZGBhwOB9LT0wNaSYYNG4Znn30WO3bswMCBA7Fx40YMGcIPh1QkMrMhRmpr+th0LSJi5CWQ61cBAKTZiHSPIMmJIrbzE8imJi0Xhpvc9n/eoEhd8AvYVvwF8l9vmN4upQQOHQQKixOfdbRqH9CnX2LLYHXeIIWrFlMHkJvn3VQm3wEcPwLoAxc3kZ4B26K12rZnwHNGJtDn7IBrrSQmn7gXX3wxLrzwQjz22GNYsmQJvva1r+Hmm7XIbNasWdi2bVvAPXl5ebjxxhsxf/58TJs2DQcOHMAPfvCDWBSHkoTo1Bli7I+0Hb91JZT5z/jOpWdE3xTpl3xLvd3XZSMdp4zdTwDk0cOQu/9n+ih12tVQ58yA3PhidGWIlZxO3k1PmnYKQdeSQpTy9I2FObkQ/UohzBYeTFIxG5MyadIkTJo0KeD4kiVLgt5z+eWXY8iQIaisrERpaWnQ8SuUusS5X4P8a2B3DPK7Qlx5LeRr64FTddpU0B5nRP7gjIyg0/HUp+YCB/cbj82eEvaR8q8vAVcnINmRrotHPr8Y8pzzIHSzocKRh6uB06fCL9WeKjwD8JklljoA0a3QN3U+M/V+hyZ87Z6ioiIMGzaMAUpHFWTmjkjP0FZUdmkre6r3T4esi2IxwiAzxaTTCehbTHqWRP7MdiKPHIJUXZD7KrRskH7BljpnRsQtS9JxCuqvboX68D2hM1GmEieDFOpA+g+CuOlOKHOeSHRJ4iLhQQp1bAEDG3uWQEz9uW9fNytHvWdyRM+UUnp/USlzfud5Ie2431Q95dZZkRc2Pf6/9OT2rVB/ORXqsgVQ590NdcnD5unajx8NPGb2vE+3erfV5xfHqpjWxiCFOhAhBJRRl3qn2qcaBimUcMr02b7tSbdCGfEt3/498wzXytOBSYsCtDh9a624U55DSm1FW7+cLaKoJ5TfLPW93t2/0VJI5+ZBuesh43Obm+F6ekFcZ9io65/TNj75IPSFJsmbTOnHsHjSX6c46fnZMEghSnoMUijhxNCRwPkXAj3ODBhpLhQblJm+zMXqXZMgGxyhH6jv6sn1DTpVny7XRr57nH2u9hrFPaE8sQbKrEcgSs+HrfxZ2H73AsR5X4eydL2WO8Pjo/dCLnneZpFm140gUJKNDZB+qwF3CFXu8UbZOYktBxG1GYMUsgTbbfdBmbsUIt1k2ui5XzPsyr+tC/2wk+6xF4qijWvRUZ95VNvIzoHy0we8x0VOrmnCN5GWDhT4pfyu3Bv69dsi2BTnfqUQ3/6eb98ZQWvOFzuM+90KW5WwSe7dBXXLm5ZN9hTglJYkUngWsSSipBWXjLNErRE08VZmFtC9B3DkEABA7q8I+RzvVOJQv6R69YWI9C/tTp2Nz68NTKkeM/7prQEgK0dLhJeeDvnPv2vHImlJ8cxg6tQZqD8JHD0MnKoHOuWFvtGPOl9bIkB0KwBKvx7VvQnhaUlL8HIGRNR2bEkhyxNCQHl4OZSfz9cOHK0Neb10r1wrznG3jNhN8mVkRT6bTGTnGFtztm/V0oB/9UVMZ8xI1QX4PU/ccBuU372gtfSkpfsSuQWZXm143ntvac+4+ApfoGUWBIV6xjFf95isNl22z3Kk050Wn2NSiJIegxRKCkIIoLN72YQQU5HVxkbI1/+s7bhHuysPL9cGxOoo426I7vXPv9D4Og/cDvWRWVB/c2dUzwlFvve2dyaPGHc9lPsWQowuMyZmcregqE/NhXRPzw7q2GHtWSO+BXRxL1FxPLpFxNQl8307bc382148a/e0w2wsIoovBimUPDzN940NkE2NppccX/Kwd9uzBovo0g2i9HzfRV0LIM7sHd1r2/zeKtUHtK/1J+F68A7IxjCDeQHIE8eCdlXJmirI1U9595Urr4UoOSewC6xqn++e1//sW0wPgGxq0lp3pNRaZTzdHrl5QJfu2jWRTl1uccI1/QeArrzyw3cg9+6CrNof/D4pDasQJwTT4hOlDAYplDwys7xdN+qCeyHdv4xkSwvUV9dA/fBfcLztHrOR3w2iew/j/YOGAgDEJVdG/9oZWcHPVe2Dung+pLMZcufHQQMW9ZFZUOfeDXXNMwG/yNU5MyIqhhg+2rstX3kB6m0/hPr+ZsjmJqi3/xDqI7O02UdNvuAFmVkQ7pYU+cn7vvu//K8hj4rBFzsAV4vx2O7/QZ1/D9QHb4es1wanyhYnXA/9FK6p34e68gnIjWuh3jlRW7jx8+0RfU8x5w7OTAdhE1FS4cBZShpCCN+A0co9UB+ZBeW2X0Fu+APkB+9AP/dE+cldAfcrt9wNfPEZ8LURAefCvvawiyDffROiXynkv17XBqDqffEZ1PtnAMe08TLKUy9BZPoCGyml95z8598AxymIKdqAVLnP2LoibvlZ8HJMnAYIBfLDd3zPfva3kM/6rpHrV/laihRFC+zy3d09O7ZBfed1iFGXQX3sl9olDy+HcM9gkgf3Ay4XZJixK+pjv9Ry2FTt8852kv/3tvGahfdDWfwnw8KO7cLTksLuHqKkx5YUSl4H9kCdPQXyg3cCTnm6egzHcvMgho6CaMXCcyItDbZZj0AZfwPExd81v+iYb0Cv3LjWu61ueRPqreMNl3rKLA/uhzrvbt8JRYEoDb4auOjUGWJMkNf3qDsBda77maoKIYRhOq589Y+AboaSet80yFN1kHXHoc69C+pv7oR8bpHvNW/6aeBrVB+A3PAHINiMLI99u0Ofj4dmDpwlShVsSaGkotz9ENSXng1YIFBPfOvy+BZC98tPWfpn4OjhgO4aeXA/5Ef/hvrKC0BNleljZHMT5CZjsjXlZ3Mh8sJMnT2rFQsFfl038Lf+ZMAsH/nWXyH/uhb+xGXfhzLqMsjOXaA+aczAKz/9wDxlf3YOkJcP1FRBfvy+af6ZuGpmSwpRqmBLCiUVUfp12B5abPyl60eJ80rF4tvfgxg+Gspt90GkpZmvSLzjIy3DbZAABQDU234I+e4m33N/eDPEgMHhXz8tDeJHtwQev/wHgceu1a4TQkD56a+9x+Ux4zRuswAFAMS3rtC+DhoKZdYjUGY/CjHGnVTuVH1AFw+6docy/xmIK3+kPXf3zrDfT8x5piBzTApR0mNLCiUlpWw81I/fNxyz/WYpzhg6AtXV1XHNjiqycyGmGRcmVO58QGtp6FkCVO4JfnO3Qi2pmv8zJ9wEcclVkZfh0u9DnPcN4NBByK3vQlz7E4jOXaBKFfLNV7SpyxNuNiasO+8bvu1DB80fnJ4B5TdLoN43TVvDSBeAeVtE+g6A3Pz3gFuVZ14BICEUG3BWP22M0N5dcE39PsTNd0EZeUnE31+bNHN2D1GqYJBCSUn0G2jcv/QqiDMSlwZdDBoKZdnLgKJAPr8Y8t//8J3sVgikpQE1VVBm/FIbM7LI2HUixlwJYY/87SiE0DLqFveCOP8C3/FrbtLyovTqC+GXYl8oitYF0twM+dp60+cq9y6A6FYI5ZEVQHqGaRZg02OT7zC+XmGx4bxc9QSgC1KkqgJSGnPA6K+vqQQ65UPk5JqeD0aqLt/ganb3ECU9dvdQ8ho8DAAgrpoI5cdTE1wYQNjtEIoC8QNfojhx812wLfg9lIeWQFn2MkSfsyEGD4XyuxeM98ZoBoxQFIg+/QICFN0Fxt0bbzee76G1nIiuBRC5wdPnK/eWa9eVjYdtxV+gXPQdv5exBTxbNjognU6oLyyFeus4qLdfa5pTRR7cD3XOTG0xySD5cILSr2nEgbNESY9BCiUt5fqZUKbfC3HVjxNdFAOR1wXKo6ugTJ8N8c1va8cUxdBSInLzgJL+2vbUn7df4fx+6YtBQ6HcMUfbURSICFdhFv0GakHXNZODX3PRdwzjZOSfn4d85zXId17XDrQ4od45EbL+pOE+9QFfcKPefq3WOhIp/QrYbEkhSnrs7qGkJbp2B7p2T3QxTIku3YChI0Neo/xsLnCoCuh9djuVKpDo0g0yvyvE1J9HPQsnXPeUEAJiwk1wHdgD7PwYcufHgMlYFnXTq0D/c7Xtza8Fnr91PJTyZyG6FoQvlGfQrN2ujY0hoqTGlhSiBBGZWVrXTLhcI7F8zSuu0TY6dYYy+1HtmBBQRnwLwpPwLcaUW+7SNtwLPwKA+N61vgv0qff/uMz0Geq9t0Ce1NZsks1NkDWV2rgWf94cKRw0S5QK2JJC1IGI70+EGDoSaM/gqFO+eTnO6gd16cOQO7bh0N03Qt50l7b0QWMDRNk4iCuvhXrnJO898i8vQv3kfS2jbIO29ICydD2g2CBsNi1oOXlCu5hdPUQpgUEKUQci0tJblwyuLa/pHwyV9NeCipxO3kPNX+4E7pvmu+eyqyGyc6E8vFybDg1oyxH4UWdO0K6/bjrkm6/4MunGcQo6EbUfdvcQUdwpdz2krSFUWOybidWpc/AbOucDAERBEcSEm8M+X/7xaUOqfwYpRKmBLSlEFHfivK/DtuzPxoP+q1R7FBQZBr2Kiy8HThyF3LIJOHcwlO9OAAqLof7sBvP7ASg33xWDUhNRojFIIaKEEGlpwMAhwP8+9R277GotGZ3+usxsiB9NAX40xXDctuIvAADX4nnApx8CAJS5S4EeZ7brYGQiih8GKUSUMLafzYUQAl1P1OLI/n0Qg4dG/Qzlpp8CVfuB/ucxOCFKMQxSiCjhMgZ+DUp+QavWXBK5ecCAQXEoFRElGgfOEhERkSUxSCEiIiJLYpBCRERElsQghYiIiCwpZgNnt27ditWrV+PIkSPo27cvZs6ciZ49e4a8Z8GCBdi2bZt3f/DgwZgzZ06sikRERERJLCZBSk1NDZYuXYqpU6eitLQUK1euxDPPPIO5c+eGvG/Pnj14/PHH0a2btrCZzcZVS4mIiEgTk+6eqqoqTJw4ESNHjkR+fj7KyspQUVER8p6jR49CSonevXsjJycHOTk5yMzMjEVxiIiIKAXEpCVl6FBjAqaDBw+iqKgo5D27d++GqqqYPn06Tp8+jaFDh2LKlCnIzc0Neo/T6YTT6fTuCyGQlZXl3Y4Vz7OYGCq5sN6SE+stebHuklMy1ZuQrcmeFEJLSwvuvvtuXHnllbjiiiuCXvfKK69g586duOGGGyCEwLJly3DWWWdh6tSpQe9Zt24d1q9f790vKSlBeXl5LItPREREFhHzIOWFF17Ap59+ikceeQR2e+QNNTt37sTChQvx7LPPBr0mWEtKbW0tWlpa2lRuPSEEioqKUFNT06oMmJQYrLfkxHpLXqy75GSFerPb7SgoKAh/XSxfdPv27di0aRPmz58fVYACANnZ2aivr4fT6URaWprpNWlpaUHPxeMHLaXkGy8Jsd6SE+stebHuklMy1FvM8qQcOnQIixYtwpQpU8JOPQaAhQsX4ssvv/TuV1RUID8/P2gQQkRERB1LTIKU5uZmLFiwAMOHD8fw4cPR2NiIxsZGSCnhcDhMu2L69OmD1atXY9euXfjoo4/w0ksvoaysLBbFISIiohQQk+6eTz75BFVVVaiqqsJbb73lPb548WI89NBDmDx5MkaMGGG4Z9y4caitrcW8efOQl5eHsrIyjB8/vlWvH23XUqKfS/HFektOrLfkxbpLTomst0hfO+YDZ4mIiIhigWv3mGhoaMC9996LhoaGRBeFosB6S06st+TFuktOyVRvDFJMSCmxZ88ey496JiPWW3JivSUv1l1ySqZ6Y5BCRERElsQghYiIiCyJQYqJtLQ0TJgwgTlbkgzrLTmx3pIX6y45JVO9cXYPERERWRJbUoiIiMiSGKQQERGRJTFIISIiIktikEJERESWxAUX/Ozfvx/Lli1DTU0NLrnkElx//fUQQiS6WARg5cqVeP311737PXr0wFNPPRWyzlifiVNfX4/Zs2fjgQceQGFhIYDQ9cF6tAazegv23gNYb1awdetWrF69GkeOHEHfvn0xc+ZM9OzZMyXeb2xJ0XE6nSgvL0dJSQkeeeQRVFZWYvPmzYkuFrl99dVXmD17NlatWoVVq1bh0UcfDVlnrM/Eqaurw4IFC1BbW+s91tq6Yj22H7N6A8zfewDrzQpqamqwdOlSTJo0CU8//TS6d++OZ555JmXebwxSdD7++GM4HA5MnjwZRUVFmDhxIt5+++1EF4sAuFwuHDhwAKWlpcjJyUFOTg6ysrJC1hnrM3GefPJJjBo1ynCstXXFemw/ZvUW7L0HsN6soKqqChMnTsTIkSORn5+PsrIyVFRUpMz7jd09Ovv27UP//v2RkZEBAOjTpw8qKysTXCoCtLqRUmLWrFk4duwYSktLceutt4asM9Zn4kybNg09evTAc8895z3W2rpiPbafYPVm9t7r3r07680Chg4datg/ePAgioqKUub9xpYUnYaGBhQUFHj3hRBQFAWnTp1KYKkI0P5a6NWrF+6880787ne/g81mw/Lly0PWGeszcXr06BFwrLV1xXpsP2b1Fuy9B7S+Tik+WlpasHHjRpSVlaXM+40tKTqKogSkCU5PT0dzc3OCSkQeo0ePxujRo737t9xyC26//XacccYZQeuM9Wktoeqjteco/oK99xwOB+vNYtauXYvMzExcdtllWLt2bUq839iSopObm4u6ujrDsYaGBtjtjOWsJicnB1JK5OfnB60z1qe1hKqP1p6j9ud57504cYL1ZiHbt2/Hpk2bcOedd7bpPWW1emOQotOvXz/s2rXLu3/48GE4nU7k5uYmsFQEAKtXr8Z7773n3d+9ezeEEOjdu3fQOmN9Wkuo+mjtOYq/YO+9bt26sd4s4tChQ1i0aBGmTJmCnj17Akid9xuDFJ2BAwfC4XDgnXfeAQC88sorGDx4MBSFP6ZEO+uss7B27Vrs3LkTO3bswKpVqzBmzBgMGTIkaJ2xPq0lVH209hzFX7D3XkZGBuvNApqbm7FgwQIMHz4cw4cPR2NjIxobG3HuueemxPuNqyD7+fDDD7Fo0SJkZWVBVVU8+OCD6NWrV6KLRQDWrFmDTZs2ITMzEyNGjMDEiRORmZkZss5Yn4l17bXXYvHixd6kYK2tK9Zj+/Kvt2DvPYD1lmgffvghHn/88YDjixcvxt69e5P+/cYgxcSxY8dQUVGBAQMGIC8vL9HFoQiEqjPWp7W0tq5Yj9bFerOuZH+/MUghIiIiS2LnIBEREVkSgxQiIiKyJAYpREREZEkMUoiIiMiSGKQQERGRJTFIISIiIktikEJERESWxCCFiIiILIlBChEREVnS/wM/bfcQvRPMHwAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#\n",
    "#拟合开始\n",
    "#\n",
    "training_set = dataf1.iloc[0:2048, 4:5].to_numpy()#要提取一列数据，否则会出错\n",
    "test_set     = dataf1.iloc[3626 - 300:, 4:5].to_numpy()\n",
    "#print(training_set)\n",
    "plt.plot(training_set)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 数据归一化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "#将数据归一化，范围是0到1\n",
    "sc  = MinMaxScaler(feature_range=(0, 1))\n",
    "training_set_scaled = sc.fit_transform(training_set)\n",
    "testing_set_scaled  = sc.transform(test_set) \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 数据分割"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 取前 n_timestamp 天的数据为 X；n_timestamp+1天数据为 Y。\n",
    "def data_split(sequence, n_timestamp):\n",
    "    X = []\n",
    "    y = []\n",
    "    for i in range(len(sequence)):\n",
    "        end_ix = i + n_timestamp\n",
    "        \n",
    "        if end_ix > len(sequence)-1:\n",
    "            break\n",
    "            \n",
    "        seq_x, seq_y = sequence[i:end_ix], sequence[end_ix]\n",
    "        X.append(seq_x)\n",
    "        y.append(seq_y)\n",
    "    return array(X), array(y)\n",
    "\n",
    "X_train, y_train = data_split(training_set_scaled, n_timestamp)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 训练数据重整"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train          = X_train.reshape(X_train.shape[0], X_train.shape[1], 1)\n",
    "\n",
    "X_test, y_test   = data_split(testing_set_scaled, n_timestamp)\n",
    "X_test           = X_test.reshape(X_test.shape[0], X_test.shape[1], 1)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 构建模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential_1\"</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1mModel: \"sequential_1\"\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃<span style=\"font-weight: bold\"> Layer (type)                    </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">       Param # </span>┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ lstm_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">LSTM</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">5</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">50</span>)          │        <span style=\"color: #00af00; text-decoration-color: #00af00\">10,400</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ lstm_3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">LSTM</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">50</span>)             │        <span style=\"color: #00af00; text-decoration-color: #00af00\">20,200</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)              │            <span style=\"color: #00af00; text-decoration-color: #00af00\">51</span> │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
       "</pre>\n"
      ],
      "text/plain": [
       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ lstm_2 (\u001b[38;5;33mLSTM\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m50\u001b[0m)          │        \u001b[38;5;34m10,400\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ lstm_3 (\u001b[38;5;33mLSTM\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m50\u001b[0m)             │        \u001b[38;5;34m20,200\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_1 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)              │            \u001b[38;5;34m51\u001b[0m │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">30,651</span> (119.73 KB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m30,651\u001b[0m (119.73 KB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">30,651</span> (119.73 KB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m30,651\u001b[0m (119.73 KB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#五、构建模型\n",
    "# 建构 LSTM模型\n",
    "model_type=2\n",
    "if model_type == 1:\n",
    "    # 单层 LSTM\n",
    "    model = Sequential()\n",
    "    model.add(LSTM(units=50, activation='relu',\n",
    "                   input_shape=(X_train.shape[1], 1)))\n",
    "    model.add(Dense(units=1))\n",
    "if model_type == 2:\n",
    "    # 多层 LSTM\n",
    "    model = Sequential()\n",
    "    model.add(LSTM(units=50, activation='relu', return_sequences=True,\n",
    "                   input_shape=(X_train.shape[1], 1)))\n",
    "    model.add(LSTM(units=50, activation='relu'))\n",
    "    model.add(Dense(1))\n",
    "if model_type == 3:\n",
    "    # 双向 LSTM\n",
    "    model = Sequential()\n",
    "    model.add(Bidirectional(LSTM(50, activation='relu'),\n",
    "                            input_shape=(X_train.shape[1], 1)))\n",
    "    model.add(Dense(1))\n",
    "\n",
    "model.summary()  # 输出模型结构"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 模型编译"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(optimizer=tf.keras.optimizers.Adam(0.001),\n",
    "              loss='mean_squared_error')  # 损失函数用均方误差\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 训练模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 15ms/step - loss: 0.0492 - val_loss: 0.0274\n",
      "Epoch 2/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.0071 - val_loss: 5.7290e-04\n",
      "Epoch 3/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 9.3557e-04 - val_loss: 7.8033e-04\n",
      "Epoch 4/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 7.7195e-04 - val_loss: 6.8099e-04\n",
      "Epoch 5/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 7.5421e-04 - val_loss: 6.3477e-04\n",
      "Epoch 6/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 7.4405e-04 - val_loss: 5.5825e-04\n",
      "Epoch 7/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 7.3251e-04 - val_loss: 5.1045e-04\n",
      "Epoch 8/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 7.2073e-04 - val_loss: 4.6550e-04\n",
      "Epoch 9/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 7.0970e-04 - val_loss: 4.4975e-04\n",
      "Epoch 10/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 6.9971e-04 - val_loss: 4.3220e-04\n",
      "Epoch 11/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 6.9052e-04 - val_loss: 4.2734e-04\n",
      "Epoch 12/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 6.8140e-04 - val_loss: 4.2166e-04\n",
      "Epoch 13/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 6.7209e-04 - val_loss: 4.1283e-04\n",
      "Epoch 14/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 6.6238e-04 - val_loss: 4.0363e-04\n",
      "Epoch 15/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 6.5274e-04 - val_loss: 3.9163e-04\n",
      "Epoch 16/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 6.4232e-04 - val_loss: 3.7321e-04\n",
      "Epoch 17/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 6.3130e-04 - val_loss: 3.5451e-04\n",
      "Epoch 18/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 6.1949e-04 - val_loss: 3.3062e-04\n",
      "Epoch 19/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 6.0714e-04 - val_loss: 3.1104e-04\n",
      "Epoch 20/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 5.9509e-04 - val_loss: 2.9134e-04\n",
      "Epoch 21/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 5.8303e-04 - val_loss: 2.7048e-04\n",
      "Epoch 22/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 5.7104e-04 - val_loss: 2.5325e-04\n",
      "Epoch 23/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 5.5858e-04 - val_loss: 2.3437e-04\n",
      "Epoch 24/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 5.4602e-04 - val_loss: 2.1890e-04\n",
      "Epoch 25/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 5.3370e-04 - val_loss: 2.0562e-04\n",
      "Epoch 26/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 5.2239e-04 - val_loss: 1.9596e-04\n",
      "Epoch 27/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 5.1082e-04 - val_loss: 1.8487e-04\n",
      "Epoch 28/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 5.0043e-04 - val_loss: 1.7638e-04\n",
      "Epoch 29/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 4.8997e-04 - val_loss: 1.6474e-04\n",
      "Epoch 30/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 4.8030e-04 - val_loss: 1.6298e-04\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential_1\"</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1mModel: \"sequential_1\"\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
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     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃<span style=\"font-weight: bold\"> Layer (type)                    </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">       Param # </span>┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ lstm_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">LSTM</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">5</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">50</span>)          │        <span style=\"color: #00af00; text-decoration-color: #00af00\">10,400</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ lstm_3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">LSTM</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">50</span>)             │        <span style=\"color: #00af00; text-decoration-color: #00af00\">20,200</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)              │            <span style=\"color: #00af00; text-decoration-color: #00af00\">51</span> │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
       "</pre>\n"
      ],
      "text/plain": [
       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ lstm_2 (\u001b[38;5;33mLSTM\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m50\u001b[0m)          │        \u001b[38;5;34m10,400\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ lstm_3 (\u001b[38;5;33mLSTM\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m50\u001b[0m)             │        \u001b[38;5;34m20,200\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_1 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)              │            \u001b[38;5;34m51\u001b[0m │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">91,955</span> (359.20 KB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m91,955\u001b[0m (359.20 KB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">30,651</span> (119.73 KB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m30,651\u001b[0m (119.73 KB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Optimizer params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">61,304</span> (239.47 KB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Optimizer params: \u001b[0m\u001b[38;5;34m61,304\u001b[0m (239.47 KB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "#七、训练模型\n",
    "history = model.fit(X_train, y_train,\n",
    "                    batch_size=64,\n",
    "                    epochs=n_epochs,\n",
    "                    validation_data=(X_test, y_test),\n",
    "                    validation_freq=1)  # 测试的epoch间隔数\n",
    "\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 输出训练结果"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(history.history['loss'], label='Training Loss')\n",
    "plt.plot(history.history['val_loss'], label='Validation Loss')\n",
    "#plt.title('Training and Validation Loss')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 模型预测"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 81ms/step\n"
     ]
    }
   ],
   "source": [
    "predicted_stock_price = model.predict(\n",
    "    X_test)                        # 测试集输入模型进行预测\n",
    "predicted_stock_price = sc.inverse_transform(\n",
    "    predicted_stock_price)  # 对预测数据还原---从（0，1）反归一化到原始范围\n",
    "real_stock_price = sc.inverse_transform(y_test)  # 对真实数据还原---从（0，1）反归一化到原始范围"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 画出真实数据和预测数据的对比曲线\n",
    "plt.plot(real_stock_price, color='red', label='Stock Price')\n",
    "plt.plot(predicted_stock_price, color='blue', label='Predicted Stock Price')\n",
    "plt.title(stockname)\n",
    "plt.xlabel('Time')\n",
    "plt.ylabel('Stock Price')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 模型校核"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "均方误差: 0.06532\n",
      "均方根误差: 0.25558\n",
      "平均绝对误差: 0.23111\n",
      "R2: 0.54568\n"
     ]
    }
   ],
   "source": [
    "MSE = metrics.mean_squared_error(predicted_stock_price, real_stock_price)\n",
    "RMSE = metrics.mean_squared_error(predicted_stock_price, real_stock_price)**0.5\n",
    "MAE = metrics.mean_absolute_error(predicted_stock_price, real_stock_price)\n",
    "R2 = metrics.r2_score(predicted_stock_price, real_stock_price)\n",
    "\n",
    "print('均方误差: %.5f' % MSE)\n",
    "print('均方根误差: %.5f' % RMSE)\n",
    "print('平均绝对误差: %.5f' % MAE)\n",
    "print('R2: %.5f' % R2)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "base",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.4"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
